FCE: A Fast Content Expression for Server-based Computing

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "FCE: A Fast Content Expression for Server-based Computing"

Transcription

1 FCE: A Fast Content Expression for Server-based Computing Qiao Li Mentor Graphics Corporation 11 Ridder Park Drive San Jose, CA 95131, U.S.A. qiao Fei Li Department of Computer Science Columbia University New York, NY 127, U.S.A. Abstract Server-based computing (SBC) is an approach delivering computational services across the network with advantages of reduced administrative costs and better resource utilization. In SBC, all application processing is done on servers and only screen updates are sent to clients. We introduce a fast content expression (FCE) for screen updates coding. Given a square region of pixel values, FCE constructs a table of unique pixel values in the region and converts each value in the original region into an index into the table. We have implemented our algorithm and compared it with other popular coding methods, including JPEG-LS, JPEG, gif, gzip, VNC hextile, and various combinations. Our results show that our approach provides low coding complexity with reasonable compression. I. INTRODUCTION In recent years, there is a growing trend away from the distributed model of desktop computing toward a more centralized model of server-based computing (SBC). In SBC, all application processing is carried out by a set of shared server machines. Clients connect to the servers for all their computing needs. Since SBC servers maintain the full persistent state of user sessions, the only functionality needed on the client is to be able to send keyboard and mouse input to the server and receive graphical display updates from the server. SBC offers the potential of reducing total cost of computational services through reduced system management cost and better utilization of shared hardware resources. The key enabling technology underlying the SBC approach is the remote display protocol, which enables graphical displays to be served across a network to a client device while applications and even window systems are executed at the server side. A number of SBC encoding techniques have been developed, ranging from higher-level graphics primitives to lowerlevel pixel-based compression techniques. However, existing SBC encoding techniques have been shown to not be effective in supporting the display demands of multimedia applications [4]. In this paper, we present fast content expression (FCE) to encode graphical display content. FCE benefits from the property of temporal and spatial similarity that neighboring pixels are correlated and therefore contain redundant information. Given a square region of pixel values, FCE constructs a table of unique pixel values in the region and represents each value in the original region by an index into the table. As any image can be decomposed into square regions, FCEs of those square regions can represent arbitrary screen update. We have implemented FCE and compared it with other popular image coding methods, including JPEG-LS, JPEG, PNG, GIF, and gzip. Our results show that our approach provides low coding complexity with reasonable compression. This paper is organized as follows. Section II discussed related work in image compression. Section III introduces the FCE expression for representing image content in a square region. Section IV describes how we apply FCE in developing a new image compression algorithm. Section V presents experimental results comparing FCE compression algorithm with other compression methods. Finally, we conclude with Section VI. II. RELATED WORK Previous approaches in encoding screen updates can be classified into two categories, graphics-based and pixel-based. Graphics-based approaches employ a variety of higher-level graphics primitives to represent screen updates in terms of fonts, lines, bitmaps, etc. These approaches are used in systems such as X, Windows Terminal Services [2], Citrix MetaFrame [1], and Tarantella [8]. Despite the range of available encoding primitives, the screen updates associated with multimedia applications such as images and video are typically encoded as raw pixel bitmaps. Little compression is achieved on these screen updates for multimedia applications. Pixel-based approaches are simpler and treat a screen update just as a region of pixels. They are used in systems such as Sun Ray [9] and Virtual Network Computing (VNC) [5]. As these approaches employ similar bitmap encoding primitives for multimedia display workloads, they limit the compression achievable for screen updates. More recently, some SBC pixel-based encoding techniques have been developed that take advantage of the common image characteristics of screen updates. These methods achieve improved compression ratios, but at the cost of higher encoding and decoding complexity. These coding costs limit the utility of these techniques for supporting multimedia applications in SBC environments. Based on image properties, different compression algorithms have different advantages. Existing algorithms with better compression ratio have higher complexity, and thus have limited applications. Hence, a simple, and yet higher compression ratio encoding method is necessary. III. FCE: FAST CONTENT EXPRESSION FCE is a general extension of the hextile compression method in VNC [5] and takes advantages of temporal and spatial correlations for screen updates by introducing a local table of content expressions.

2 FCE is an expression with varying length to describe screen update content in a square region. Suppose the update square region has s along each edge, and among the s 2 pixel values, there are A totally different values (A s 2 ). If we use a queue to hold all the A different values, then log 2 (A) bits are enough to express the offset i into the queue for any specific value in the update region. Namely each pixel value and its offset i has an 1 1 mapping relation in the square region. Thus FCE can use the offsets for the values in the queue instead of full pixel values. The format of FCE is shown in Fig. 1. Fig. 1. Field 1 Field 2 Field 3 FCE format to represent image content in a square region. Each FCE consists of 3 fields. 1. Field 1 (number of different pixel values): This field contains the value A, which indicates how many different pixel values are in this square region. It helps traversing field 2 to extract all pixel values and implies the boundary of field 2 and field 3 in FCE. The value of A depends on the spatial redundancy of the square region in the image. 2. Field 2 (queue of different pixel values): This field is the queue holding all the A different pixel values. The length of this field depends on the value in the first field (A) and number of bits used to store one full pixel value in the machine. 3. Field 3 (offset for all pixels in scan-line order): For each pixel, it uses the offset of its value in the queue (field 2)to denote its pixel value. As there are A different values, each pixel will use up to log 2 (A) bits for the offset. Field 3 contains all the offsets for all the pixels in the square region, and has a length of s 2 log 2 (A). FCE records these offsets in a scan-line order. An example of writing a FCE expression for a 4 4 image is shown in Fig. 2. In this figure, the sample image is divided FCE expressions If we use m bits to represent the A ( log 2 (A) m) different pixel values, n bits to represent each different full pixel value, the length of FCE, L, represented in bits, for this square region with edge size of s will be, L = m + A n + s 2 log 2 (A) (1) For ease of extracting the first field in FCE, we make m as a constant that is not less than log 2 (s 2 ) as A s 2. As FCE has a header composed of a pixel value queue and A, its length may be longer than using full pixel values for each pixel presentation. Whether FCE representation is shorter depends on the size of the square region s and the number of different pixel values A, which reflects the temporal redundancies for the images. Hence, if we want to make sure that L is shorter than that used by full pixel value presentation, we need 2 log 2 (s) + A n + s 2 log 2 (A) s 2 n (2) If we let n =8for the inequality, as used in most machines, the figure shown in Fig. 3 will reflect the relation between A and s. Only those values under the curve should be chosen for A to satisfy Equ. 2. And at the same time, if we know the pixel value distribution of the image, we will be able to select square edge size s. That is, if we denote β as the compression ratio, we have, β(a, s) = s 2 n 2 log 2 (s) + A n + s 2 log 2 (A) From Equ. 3, the compression ratio β depends on the square size s and A. The optimal size s for the squares is where the highest compression ratio is attained. Suppose the density (the ratio of the number of different pixel values and the total number of pixels) of an image is.1, which is typical for a smoothtoned image, this relation between ratio gain and s is shown in Fig. 4. We can only choose those values under the curve for compression ratio β and s. In order to satisfy Equ. 2 and gain as much compression ratio β as possible, we need to find the crossing point for these two curves in Fig. 3 and Fig. 4. We can see that 16 is one of the best choices for image in FCE format with shortest length. That means Equ. 2 and Equ. 3 sets up the theoretical basis for us to choose the square edge size. (3) Relation between s and A relation.dat 12 Fig. 2. Compressed file: send to gzip for further compression Sample of using FCE for image compression. compressed file number of different pixel values into 4 squares whose edge is of 2-pixel long. There are 4 FCE expressions corresponding to these 4 squares. Each FCE uses its own pixel value queue to represent the pixel values in the 2 2 square. Fig square edge size s Relation between square edge and number of different pixel value

3 Fig times of ratio Relation between s and ratio square edge size s Relation between square edge and ratio relation2.dat IV. COMPRESSION ALGORITHM From the discussions on FCE format above, we know that, if the a region can be approximated with fewer different pixel values, we can use the FCE expression to compress it. The basis for this approach is the assumption that neighboring pixel values have much similarity. A. Compression Algorithm From discussion from Section III, we choose 16 for s. Assume the give image has 3 planes, called R, G, and B. The outline of the compression algorithm is as follows, 1. For each RGB plane, do the following: 1.1 Decompose image on the plane into squares of size 16 16; 1.2 Allocate a matrix M r,g,b to store offsets for all pixel values; 1.3 For each square i, allocate a queue Q i to store different pixel values in this square. 2. For each square i in each plane, do the following, 2.1 Put all different pixel values in square i into queue Q i ; 2.2 Record offsets for all pixel values in square i, into M r,g,b. 3. Record M r,g,b and all Qs for each plane. 4. Use gzip to further compress M and Q. An pseudocode of the algorithm that describe this procedure is given below. Let the number of pixels be S, we allocate matrix R, G, B of size S to contain pixel R, G, and B values, we also allocate offset matrix M r, M g, and M b of size S to hold pixel value offsets in the corresponding square queues; For each square, we allocate a pixel value queue Q. And, for each queue Q, a corresponding array A, which contains number of different pixel values, is initialized to all s. for each R, G, and B planes do for the ith and jth square do if RGB value in this square in Q i,j then record offset in M; else insert value in Q i,j ; A i,j ++; record offset in M; end if for each square in each planes do write A i,j into compressed file; write Q i,j into compressed file; write M into compressed file; Using gzip for each compressed file in each plane. An example showing how the algorithm works on previous sample is shown in Fig. 2. It shows the case for one plane. We put A value for each square first in the compressed file, then, we put all different pixel values into the file, and the offset for each pixel value follows. There are three issues for us to notice in our algorithm, which effectively make the file containing FCE expressions favor gzip compression. 1. In order to make the file containing FCE expressions better for gzip compression, we put FCE expressions for each plane one by one. And for each plane, instead of putting FCE for each square one by one, we put the pixel values for all the squares together, then all the offsets. This approach under gzip attains better compression. It depends on the property that neighboring square may have similar number of total different pixel values and offsets. 2. We output offsets as bits instead of as bytes in order to save space. When neighboring pixels are similar to each other, FCE encoding gains a lot of compression because each pixel just needs log 2 (A) bits. If A is 1, the whole square region just needs one unit of pixel value in FCE expression. 3. For ease of decoding pixel values, we fix the size of A to max(a i ) for all the squares. In our experiments, we employ log 2 (s 2 ) bits, which is s bytes, for A. From the discussion above, we know that the advantages of using FCE is based on the assumption that the density for different pixel values in the image is not large. Given one image with its content of RGB values, the question is whether it is possible to convert to a representation with even less density. If the conversion is good enough, we can achieve a near-lossless solution with even better compression ratio. B. Color Space Plane Conversion The formula is based on Julien s work [6]. The YUV planes give more similarity among the values for each square. That is, Y =.299 R G B (4) with its reciprocal versions: U =(B Y ).565 (5) V =(R Y ).713 (6) R = Y V (7) G = Y.344 U.714 V (8) B = Y U (9) Consider the color space plane conversions, we have some experimental data to show that conversion from RGB planes to

4 airplane baboon fruits lena peppers A (R plane) var (R plane) A (G plane) var (G plane) A (B plane) var (B plane) A (Y plane) var (Y plane) A (U plane) var (U plane) A (V plane) var (V plane) TABLE I COLOR PLANE CONVERSION STATISTICS YUV planes can make neighboring pixel values more similar to each other in the following table. The table shows how many different pixel values A for each plane and what the variance var is for each square of From the table, we can see that Y plane has similar A value and variance, compared with R plane. However, U and V planes have very smaller A and variances, compared with G and B planes for the same images. This assures us that this conversion is good for FCE expressions in lossy compression. Also, for the conversion from RGB to YUV in the formula, encoding and decoding result in a loss, however the error is bounded to be less than 5 for all 256 possible values each color can assume. As this error hardly has any visual effect, the conversion is near-lossless, and the converted data is more amenable to compression. V. EXPERIMENTAL RESULTS To evaluate the performance of using FCE, we compared its compression performance and coding complexity against several other popular compression methods for toned-images. For desktop-like screen updates, FCE gains much over compression algorithms favoring toned-images. We only consider multimedia application coding only. These methods include JPEG-LS, lossless JPEG using Huffman coding, PNG, gzip, lossy JPEG from IJG with quality equal to 1, and GIF, which is lossy due to the conversion from 24-bit to 8-bit color. We show results for using FCE in both RGB and YUV color space. We used 5 different images for our measurements, which are from a standard collection of test images [7]. The measurements were performed on an IBM NetVista PC with a 1 GHz AMD Athlon CPU and 256 MB RAM, running RedHat Linux 7.1. Table II shows the compression results in terms of total image size after compression for each compression algorithm on each of the 5 images. Table III shows the corresponding compression ratio for each algorithm. Tables IV and V show the coding complexity results in terms of encoding and decoding time, respectively for each compression algorithm on each of the 5 images. Tables II and III show that FCE consistently achieves much faster encoding and modest decoding with similar compression ratio. Compared with OLI [3], FCE gains better compression ratio and faster coding for multimedia applications. We also measured the performance of FCE-RGB and FCE-YUV with other tile sizes. Our measurements confirmed our earlier analysis that showed a size square region for FCE expressions provided the best FCE compression performance. VI. CONCLUSIONS AND FUTURE WORK In this paper, we first introduce a fast content expression to describe screen updates content. Then, we apply it to improve lossless and near-lossless compression for multimedia applications. The new lossless image compression algorithm integrates well with the popular gzip compression utility. We have implemented our algorithm and compared it with other popular coding methods, including JPEG-LS, JPEG, gif, gzip, and various combinations. Our results show that our approach provides superior coding complexity with modest compression, which gains its suitability in server-based computing. REFERENCES [1] Citrix Systems, Citrix MetaFrame 1.8 Backgrounder Citrix While Paper, June [2] B. C. Cumberland and G. Carius, Microsoft Windows NT Server 4., Terminal Server Edition: Technical Reference Microsoft Press, Redmond, WA, August [3] F. Li and J. Nieh, Optimal Linear Interpolation for Server-based Computing, in Proceedings of IEEE International Conference on Communications, New York, NY, USA, April 22, pp [4] J. Nieh and S. J. Yang, Measuring the Multimedia Performance of Server- Based Computing Proceedings of the Tenth International Workshop on Network and Operating System Support for Digital Audio and Video, Chapel Hill, NC, June 2. [5] T. Richardson, Q. Stafford-Fraser, K. R. Wood and A. Hopper, Virtual Network Computing IEEE Internet Computing, Vol. 2, No. 1, January/February [6] YUV conversion, [7] Standard test images, [8] The Santa Cruz Operation, Tarantella Web-Enabling Software: The Adaptive Internet Protocol A SCO Technical While Paper, December [9] Sun Ray 1 Enterprise Appliance, Sun Microsystems

5 airplane baboon fruits lena peppers no compression 786, , , , ,432 JPEG-LS 387,964 66,71 46,58 446, ,448 JPEG Huffman 45,33 54,577 59,636 56,269 55,798 PNG 475, ,65 491,67 525, ,653 gzip 577, , , , ,298 JPEG (IJG quality = 1) 213,83 342, , , ,633 GIF 298,66 298,66 298,66 298,66 298,66 FCE-RGB 475, ,87 487, , ,592 FCE-YUV 391, ,61 41,44 452, TABLE II IMAGE SIZE IN BYTES AFTER COMPRESSION airplane baboon fruits lena peppers AVERAGE JPEG-LS JPEG Huffman PNG gzip JPEG (IJG quality = 1) GIF FCE-RGB FCE-YUV TABLE III COMPRESSION RATIOS airplane baboon fruits lena peppers JPEG-LS JPEG Huffman PNG gzip JPEG (IJG quality = 1) GIF FCE-RGB FCE-YUV TABLE IV ENCODING TIME COMPLEXITY IN MILLISECONDS airplane baboon fruits lena peppers JPEG-LS JPEG Huffman PNG gunzip JPEG (IJG quality = 1) GIF FCE-RGB FCE-YUV TABLE V DECODING TIME COMPLEXITY IN MILLISECONDS.

Comparison of different image compression formats. ECE 533 Project Report Paula Aguilera

Comparison of different image compression formats. ECE 533 Project Report Paula Aguilera Comparison of different image compression formats ECE 533 Project Report Paula Aguilera Introduction: Images are very important documents nowadays; to work with them in some applications they need to be

More information

Video compression: Performance of available codec software

Video compression: Performance of available codec software Video compression: Performance of available codec software Introduction. Digital Video A digital video is a collection of images presented sequentially to produce the effect of continuous motion. It takes

More information

Introduction to image coding

Introduction to image coding Introduction to image coding Image coding aims at reducing amount of data required for image representation, storage or transmission. This is achieved by removing redundant data from an image, i.e. by

More information

Screen Capture A Vector Quantisation Approach

Screen Capture A Vector Quantisation Approach Screen Capture A Vector Quantisation Approach Jesse S. Jin and Sue R. Wu Biomedical and Multimedia Information Technology Group School of Information Technologies, F09 University of Sydney, NSW, 2006 {jesse,suewu}@it.usyd.edu.au

More information

Compression and Image Formats

Compression and Image Formats Compression Compression and Image Formats Reduce amount of data used to represent an image/video Bit rate and quality requirements Necessary to facilitate transmission and storage Required quality is application

More information

Image Compression through DCT and Huffman Coding Technique

Image Compression through DCT and Huffman Coding Technique International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul

More information

Server based computing An introduction to server based computing, its advantages and how it works.

Server based computing An introduction to server based computing, its advantages and how it works. Server based computing An introduction to server based computing, its advantages and how it works. This whitepaper explains the concept of server based computing and defines the main components of its

More information

Thin Clients and PCs A comparative study to find suitability for different computing environments

Thin Clients and PCs A comparative study to find suitability for different computing environments Thin Clients and PCs A comparative study to find suitability for different computing environments Tanmay K. Mohapatra Choosing between thin clients and PCs requires a rational evaluation. Often a correct

More information

MMGD0203 Multimedia Design MMGD0203 MULTIMEDIA DESIGN. Chapter 3 Graphics and Animations

MMGD0203 Multimedia Design MMGD0203 MULTIMEDIA DESIGN. Chapter 3 Graphics and Animations MMGD0203 MULTIMEDIA DESIGN Chapter 3 Graphics and Animations 1 Topics: Definition of Graphics Why use Graphics? Graphics Categories Graphics Qualities File Formats Types of Graphics Graphic File Size Introduction

More information

ANALYSIS OF THE EFFECTIVENESS IN IMAGE COMPRESSION FOR CLOUD STORAGE FOR VARIOUS IMAGE FORMATS

ANALYSIS OF THE EFFECTIVENESS IN IMAGE COMPRESSION FOR CLOUD STORAGE FOR VARIOUS IMAGE FORMATS ANALYSIS OF THE EFFECTIVENESS IN IMAGE COMPRESSION FOR CLOUD STORAGE FOR VARIOUS IMAGE FORMATS Dasaradha Ramaiah K. 1 and T. Venugopal 2 1 IT Department, BVRIT, Hyderabad, India 2 CSE Department, JNTUH,

More information

A Preprocessing Algorithm for Efficient Lossless Compression of Gray Scale Images

A Preprocessing Algorithm for Efficient Lossless Compression of Gray Scale Images A Preprocessing Algorithm for Efficient Lossless Compression of Gray Scale Images Sun-Ja Kim*, Doh-Yeun Hwang **, Gi-Hyoung Yoo**, Kang-Soo You*** and Hoon-Sung Kwak** * Department of Industrial Technology,

More information

Improving Web Browsing on Wireless PDAs Using Thin-Client Computing

Improving Web Browsing on Wireless PDAs Using Thin-Client Computing Improving Web Browsing on Wireless PDAs Using Thin-Client Computing Albert M. Lai, Jason Nieh, Bhagyashree Bohra, Vijayarka Nandikonda, Abhishek P. Surana, and Suchita Varshneya Department of Computer

More information

International Journal of Emerging Technology and Advanced Engineering Website: (ISSN , Volume 2, Issue 4, April 2012)

International Journal of Emerging Technology and Advanced Engineering Website:  (ISSN , Volume 2, Issue 4, April 2012) A Low Space Bit-Plane Slicing Based Image Storage Method using Extended JPEG Format Santanu Halder 1, Debotosh Bhattacharjee 2, Mita Nasipuri 2, Dipak Kumar Basu 2 1 Department of Computer Science and

More information

JPEG Image Compression by Using DCT

JPEG Image Compression by Using DCT International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Issue-4 E-ISSN: 2347-2693 JPEG Image Compression by Using DCT Sarika P. Bagal 1* and Vishal B. Raskar 2 1*

More information

Computer Graphics in Medicine

Computer Graphics in Medicine Computer Graphics in Medicine Loren Block For my presentation, I will discuss several different papers that are related to graphics and imaging in the medical field. The main areas that the papers cover

More information

balesio Native Format Optimization Technology (NFO)

balesio Native Format Optimization Technology (NFO) balesio AG balesio Native Format Optimization Technology (NFO) White Paper Abstract balesio provides the industry s most advanced technology for unstructured data optimization, providing a fully system-independent

More information

Hardware Compression in Storage Networks and Network Attached Storage

Hardware Compression in Storage Networks and Network Attached Storage Hardware Compression in Storage Networks and Network Attached Storage Tony Summers, Comtech AHA April 2007 SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA. Member companies

More information

Secured Lossless Medical Image Compression Based On Adaptive Binary Optimization

Secured Lossless Medical Image Compression Based On Adaptive Binary Optimization IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. IV (Mar-Apr. 2014), PP 43-47 Secured Lossless Medical Image Compression Based On Adaptive Binary

More information

Getting Started with RemoteFX in Windows Embedded Compact 7

Getting Started with RemoteFX in Windows Embedded Compact 7 Getting Started with RemoteFX in Windows Embedded Compact 7 Writers: Randy Ocheltree, Ryan Wike Technical Reviewer: Windows Embedded Compact RDP Team Applies To: Windows Embedded Compact 7 Published: January

More information

Performance analysis and comparison of virtualization protocols, RDP and PCoIP

Performance analysis and comparison of virtualization protocols, RDP and PCoIP Performance analysis and comparison of virtualization protocols, RDP and PCoIP Jiri Kouril, Petra Lambertova Department of Telecommunications Brno University of Technology Ustav telekomunikaci, Purkynova

More information

B2.53-R3: COMPUTER GRAPHICS. NOTE: 1. There are TWO PARTS in this Module/Paper. PART ONE contains FOUR questions and PART TWO contains FIVE questions.

B2.53-R3: COMPUTER GRAPHICS. NOTE: 1. There are TWO PARTS in this Module/Paper. PART ONE contains FOUR questions and PART TWO contains FIVE questions. B2.53-R3: COMPUTER GRAPHICS NOTE: 1. There are TWO PARTS in this Module/Paper. PART ONE contains FOUR questions and PART TWO contains FIVE questions. 2. PART ONE is to be answered in the TEAR-OFF ANSWER

More information

Terminal Server Software and Hardware Requirements. Terminal Server. Software and Hardware Requirements. Datacolor Match Pigment Datacolor Tools

Terminal Server Software and Hardware Requirements. Terminal Server. Software and Hardware Requirements. Datacolor Match Pigment Datacolor Tools Terminal Server Software and Hardware Requirements Datacolor Match Pigment Datacolor Tools January 21, 2011 Page 1 of 8 Introduction This document will provide preliminary information about the both the

More information

Benchmarking the Performance of XenDesktop Virtual DeskTop Infrastructure (VDI) Platform

Benchmarking the Performance of XenDesktop Virtual DeskTop Infrastructure (VDI) Platform Benchmarking the Performance of XenDesktop Virtual DeskTop Infrastructure (VDI) Platform Shie-Yuan Wang Department of Computer Science National Chiao Tung University, Taiwan Email: shieyuan@cs.nctu.edu.tw

More information

Image Compression and Decompression using Adaptive Interpolation

Image Compression and Decompression using Adaptive Interpolation Image Compression and Decompression using Adaptive Interpolation SUNILBHOOSHAN 1,SHIPRASHARMA 2 Jaypee University of Information Technology 1 Electronicsand Communication EngineeringDepartment 2 ComputerScience

More information

Server Based Desktop Virtualization with Mobile Thin Clients

Server Based Desktop Virtualization with Mobile Thin Clients Server Based Desktop Virtualization with Mobile Thin Clients Prof. Sangita Chaudhari Email: sangita123sp@rediffmail.com Amod N. Narvekar Abhishek V. Potnis Pratik J. Patil Email: amod.narvekar@rediffmail.com

More information

VirtuMob : Remote Display Virtualization Solution For Smartphones

VirtuMob : Remote Display Virtualization Solution For Smartphones VirtuMob : Remote Display Virtualization Solution For Smartphones M H Soorajprasad #1, Balapradeep K N #2, Dr. Antony P J #3 #1 M.Tech Student, Department of CS&E,KVGCE Sullia, India #2 Assistant Professor,

More information

Friendly Medical Image Sharing Scheme

Friendly Medical Image Sharing Scheme Journal of Information Hiding and Multimedia Signal Processing 2014 ISSN 2073-4212 Ubiquitous International Volume 5, Number 3, July 2014 Frily Medical Image Sharing Scheme Hao-Kuan Tso Department of Computer

More information

VMware View 4 with PCoIP I N F O R M AT I O N G U I D E

VMware View 4 with PCoIP I N F O R M AT I O N G U I D E VMware View 4 with PCoIP I N F O R M AT I O N G U I D E Table of Contents VMware View 4 with PCoIP................................................... 3 About This Guide........................................................

More information

Data Storage 3.1. Foundations of Computer Science Cengage Learning

Data Storage 3.1. Foundations of Computer Science Cengage Learning 3 Data Storage 3.1 Foundations of Computer Science Cengage Learning Objectives After studying this chapter, the student should be able to: List five different data types used in a computer. Describe how

More information

Why use ColorGauge Micro Analyzer with the Micro and Nano Targets?

Why use ColorGauge Micro Analyzer with the Micro and Nano Targets? Image Science Associates introduces a new system to analyze images captured with our 30 patch Micro and Nano targets. Designed for customers who require consistent image quality, the ColorGauge Micro Analyzer

More information

Improved N Level Decomposition-Hybrid DCT-DWT Image Compression

Improved N Level Decomposition-Hybrid DCT-DWT Image Compression International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-6, June 2014 Improved N Level Decomposition-Hybrid DCT-DWT Image Compression Sangeeta Abstract With

More information

On the Performance of Wide-Area Thin-Client Computing

On the Performance of Wide-Area Thin-Client Computing ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Computer

More information

CS101 Lecture 13: Image Compression. What You ll Learn Today

CS101 Lecture 13: Image Compression. What You ll Learn Today CS101 Lecture 13: Image Compression Vector Graphics Compression Techniques Aaron Stevens (azs@bu.edu) 22 February 2013 What You ll Learn Today Review: how big are image files? How can we make image files

More information

A Comparison of Thin-Client Computing Architectures

A Comparison of Thin-Client Computing Architectures A Comparison of Thin-Client Computing Architectures Technical Report CUCS-022-00 November 2000 Jason Nieh, S. Jae Yang, Naomi Novik Network Computing Laboratory, Columbia University {nieh, sy180, nn80}@cs.columbia.edu

More information

encoding compression encryption

encoding compression encryption encoding compression encryption ASCII utf-8 utf-16 zip mpeg jpeg AES RSA diffie-hellman Expressing characters... ASCII and Unicode, conventions of how characters are expressed in bits. ASCII (7 bits) -

More information

Introduction to Multimedia What is Multimedia?

Introduction to Multimedia What is Multimedia? Introduction to Multimedia What is Multimedia? 22 What is Multimedia? Multimedia can have many definitions these include: (A computer system perspective) 23 Multimedia means that computer information can

More information

Transmitting Video Images in XML Web Service

Transmitting Video Images in XML Web Service Transmitting Video Images in XML Web Service Francisco Prieto, Antonio J. Sierra, María Carrión García Departamento de Ingeniería de Sistemas y Automática Área de Ingeniería Telemática Escuela Superior

More information

AdminToys Suite. Installation & Setup Guide

AdminToys Suite. Installation & Setup Guide AdminToys Suite Installation & Setup Guide Copyright 2008-2009 Lovelysoft. All Rights Reserved. Information in this document is subject to change without prior notice. Certain names of program products

More information

Web Graph Visualizer. AUTOMATYKA 2011 Tom 15 Zeszyt 3. 1. Introduction. Micha³ Sima*, Wojciech Bieniecki*, Szymon Grabowski*

Web Graph Visualizer. AUTOMATYKA 2011 Tom 15 Zeszyt 3. 1. Introduction. Micha³ Sima*, Wojciech Bieniecki*, Szymon Grabowski* AUTOMATYKA 2011 Tom 15 Zeszyt 3 Micha³ Sima*, Wojciech Bieniecki*, Szymon Grabowski* Web Graph Visualizer 1. Introduction Web Graph is a directed, unlabeled graph G = (V, E), which represents connections

More information

Digital Image Fundamentals. Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr

Digital Image Fundamentals. Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Imaging process Light reaches surfaces in 3D. Surfaces reflect. Sensor element receives

More information

Reading.. IMAGE COMPRESSION- I IMAGE COMPRESSION. Image compression. Data Redundancy. Lossy vs Lossless Compression. Chapter 8.

Reading.. IMAGE COMPRESSION- I IMAGE COMPRESSION. Image compression. Data Redundancy. Lossy vs Lossless Compression. Chapter 8. Reading.. IMAGE COMPRESSION- I Week VIII Feb 25 Chapter 8 Sections 8.1, 8.2 8.3 (selected topics) 8.4 (Huffman, run-length, loss-less predictive) 8.5 (lossy predictive, transform coding basics) 8.6 Image

More information

Hybrid Lossless Compression Method For Binary Images

Hybrid Lossless Compression Method For Binary Images M.F. TALU AND İ. TÜRKOĞLU/ IU-JEEE Vol. 11(2), (2011), 1399-1405 Hybrid Lossless Compression Method For Binary Images M. Fatih TALU, İbrahim TÜRKOĞLU Inonu University, Dept. of Computer Engineering, Engineering

More information

PERFORMANCE ANALYSIS OF HIGH RESOLUTION IMAGES USING INTERPOLATION TECHNIQUES IN MULTIMEDIA COMMUNICATION SYSTEM

PERFORMANCE ANALYSIS OF HIGH RESOLUTION IMAGES USING INTERPOLATION TECHNIQUES IN MULTIMEDIA COMMUNICATION SYSTEM PERFORMANCE ANALYSIS OF HIGH RESOLUTION IMAGES USING INTERPOLATION TECHNIQUES IN MULTIMEDIA COMMUNICATION SYSTEM Apurva Sinha 1, Mukesh kumar 2, A.K. Jaiswal 3, Rohini Saxena 4 Department of Electronics

More information

MassArt Studio Foundation: Visual Language Digital Media Cookbook, Fall 2013

MassArt Studio Foundation: Visual Language Digital Media Cookbook, Fall 2013 INPUT OUTPUT 08 / IMAGE QUALITY & VIEWING In this section we will cover common image file formats you are likely to come across and examine image quality in terms of resolution and bit depth. We will cover

More information

Less naive Bayes spam detection

Less naive Bayes spam detection Less naive Bayes spam detection Hongming Yang Eindhoven University of Technology Dept. EE, Rm PT 3.27, P.O.Box 53, 5600MB Eindhoven The Netherlands. E-mail:h.m.yang@tue.nl also CoSiNe Connectivity Systems

More information

Outline: Operating Systems

Outline: Operating Systems Outline: Operating Systems What is an OS OS Functions Multitasking Virtual Memory File Systems Window systems PC Operating System Wars: Windows vs. Linux 1 Operating System provides a way to boot (start)

More information

IMAGE COMPRESSION BY EMBEDDING FIVE MODULUS METHOD INTO JPEG

IMAGE COMPRESSION BY EMBEDDING FIVE MODULUS METHOD INTO JPEG IMAGE COMPRESSION BY EMBEDDING FIVE MODULUS METHOD INTO JPEG Firas A. Jassim Management Information Systems Department, Irbid National University, Irbid 2600, Jordan Firasajil@yahoo.com ABSTRACT The standard

More information

Infor Web UI Sizing and Deployment for a Thin Client Solution

Infor Web UI Sizing and Deployment for a Thin Client Solution Infor Web UI Sizing and Deployment for a Thin Client Solution Copyright 2012 Infor Important Notices The material contained in this publication (including any supplementary information) constitutes and

More information

On the Performance of Wide-Area Thin-Client Computing

On the Performance of Wide-Area Thin-Client Computing On the Performance of Wide-Area Thin-Client Computing ALBERT M. LAI and JASON NIEH Columbia University While many application service providers have proposed using thin-client computing to deliver computational

More information

Compression techniques

Compression techniques Compression techniques David Bařina February 22, 2013 David Bařina Compression techniques February 22, 2013 1 / 37 Contents 1 Terminology 2 Simple techniques 3 Entropy coding 4 Dictionary methods 5 Conclusion

More information

Data Storage. Chapter 3. Objectives. 3-1 Data Types. Data Inside the Computer. After studying this chapter, students should be able to:

Data Storage. Chapter 3. Objectives. 3-1 Data Types. Data Inside the Computer. After studying this chapter, students should be able to: Chapter 3 Data Storage Objectives After studying this chapter, students should be able to: List five different data types used in a computer. Describe how integers are stored in a computer. Describe how

More information

Remote desktop protocols. A comparison of Spice, NX and VNC. Bachelor Degree Project in Network and System administration G2E 15h Spring term 2012

Remote desktop protocols. A comparison of Spice, NX and VNC. Bachelor Degree Project in Network and System administration G2E 15h Spring term 2012 Remote desktop protocols A comparison of Spice, NX and VNC Bachelor Degree Project in Network and System administration G2E 15h Spring term 2012 Martin Hagström h06marha@student.his.se June 5, 2012 Supervisor:

More information

Study and Implementation of Video Compression standards (H.264/AVC, Dirac)

Study and Implementation of Video Compression standards (H.264/AVC, Dirac) Study and Implementation of Video Compression standards (H.264/AVC, Dirac) EE 5359-Multimedia Processing- Spring 2012 Dr. K.R Rao By: Sumedha Phatak(1000731131) Objective A study, implementation and comparison

More information

HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER

HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER Gholamreza Anbarjafari icv Group, IMS Lab, Institute of Technology, University of Tartu, Tartu 50411, Estonia sjafari@ut.ee

More information

A sustainable networking architecture ~ progress on the Ndiyo Project

A sustainable networking architecture ~ progress on the Ndiyo Project A sustainable networking architecture ~ progress on the Ndiyo Project Sebastian Wills John Naughton Quentin Stafford-Fraser Newnham Research Ltd. The problem Expensive Unnecessarily replicated components

More information

Structures for Data Compression Responsible persons: Claudia Dolci, Dante Salvini, Michael Schrattner, Robert Weibel

Structures for Data Compression Responsible persons: Claudia Dolci, Dante Salvini, Michael Schrattner, Robert Weibel Geographic Information Technology Training Alliance (GITTA) presents: Responsible persons: Claudia Dolci, Dante Salvini, Michael Schrattner, Robert Weibel Content 1.... 2 1.1. General Compression Concepts...3

More information

Windows Embedded Compact 7: RemoteFX and Remote Experience Thin Client Integration

Windows Embedded Compact 7: RemoteFX and Remote Experience Thin Client Integration Windows Embedded Compact 7: RemoteFX and Remote Experience Thin Client Integration Windows Embedded Technical Article Summary: Microsoft RemoteFX is a new Windows Server 2008 R2 SP1 feature that enables

More information

NComputing desktop virtualization

NComputing desktop virtualization NComputing Abstract We ve all become accustomed to the PC model, which allows every user to have their own CPU, hard disk, and memory to run their applications. But personal computers have now become so

More information

Information, Entropy, and Coding

Information, Entropy, and Coding Chapter 8 Information, Entropy, and Coding 8. The Need for Data Compression To motivate the material in this chapter, we first consider various data sources and some estimates for the amount of data associated

More information

Image Content-Based Email Spam Image Filtering

Image Content-Based Email Spam Image Filtering Image Content-Based Email Spam Image Filtering Jianyi Wang and Kazuki Katagishi Abstract With the population of Internet around the world, email has become one of the main methods of communication among

More information

Mobile Virtual Network Computing System

Mobile Virtual Network Computing System Mobile Virtual Network Computing System Vidhi S. Patel, Darshi R. Somaiya Student, Dept. of I.T., K.J. Somaiya College of Engineering and Information Technology, Mumbai, India ABSTRACT: we are planning

More information

A comprehensive survey on various ETC techniques for secure Data transmission

A comprehensive survey on various ETC techniques for secure Data transmission A comprehensive survey on various ETC techniques for secure Data transmission Shaikh Nasreen 1, Prof. Suchita Wankhade 2 1, 2 Department of Computer Engineering 1, 2 Trinity College of Engineering and

More information

MICROSOFT. Remote Desktop Protocol Performance Improvements in Windows Server 2008 R2 and Windows 7

MICROSOFT. Remote Desktop Protocol Performance Improvements in Windows Server 2008 R2 and Windows 7 MICROSOFT Remote Desktop Protocol Performance Improvements in Windows Server 2008 R2 and Windows 7 Microsoft Corporation January 2010 Copyright This document is provided as-is. Information and views expressed

More information

Jozef Matula. Visualisation Team Leader IBL Software Engineering. 13 th ECMWF MetOps Workshop, 31 th Oct - 4 th Nov 2011, Reading, United Kingdom

Jozef Matula. Visualisation Team Leader IBL Software Engineering. 13 th ECMWF MetOps Workshop, 31 th Oct - 4 th Nov 2011, Reading, United Kingdom Visual Weather web services Jozef Matula Visualisation Team Leader IBL Software Engineering Outline Visual Weather in a nutshell. Path from Visual Weather (as meteorological workstation) to Web Server

More information

Epiphan Frame Grabber User Guide

Epiphan Frame Grabber User Guide Epiphan Frame Grabber User Guide VGA2USB VGA2USB LR DVI2USB VGA2USB HR DVI2USB Solo VGA2USB Pro DVI2USB Duo KVM2USB www.epiphan.com 1 February 2009 Version 3.20.2 (Windows) 3.16.14 (Mac OS X) Thank you

More information

Hardware Configuration Guide

Hardware Configuration Guide Hardware Configuration Guide Contents Contents... 1 Annotation... 1 Factors to consider... 2 Machine Count... 2 Data Size... 2 Data Size Total... 2 Daily Backup Data Size... 2 Unique Data Percentage...

More information

Performance Analysis and Comparison of JM 15.1 and Intel IPP H.264 Encoder and Decoder

Performance Analysis and Comparison of JM 15.1 and Intel IPP H.264 Encoder and Decoder Performance Analysis and Comparison of 15.1 and H.264 Encoder and Decoder K.V.Suchethan Swaroop and K.R.Rao, IEEE Fellow Department of Electrical Engineering, University of Texas at Arlington Arlington,

More information

1. Redistributions of documents, or parts of documents, must retain the SWGIT cover page containing the disclaimer.

1. Redistributions of documents, or parts of documents, must retain the SWGIT cover page containing the disclaimer. Disclaimer: As a condition to the use of this document and the information contained herein, the SWGIT requests notification by e-mail before or contemporaneously to the introduction of this document,

More information

Degree Reduction of Interval SB Curves

Degree Reduction of Interval SB Curves International Journal of Video&Image Processing and Network Security IJVIPNS-IJENS Vol:13 No:04 1 Degree Reduction of Interval SB Curves O. Ismail, Senior Member, IEEE Abstract Ball basis was introduced

More information

CONFIGURATION GUIDELINES: EMC STORAGE FOR PHYSICAL SECURITY

CONFIGURATION GUIDELINES: EMC STORAGE FOR PHYSICAL SECURITY White Paper CONFIGURATION GUIDELINES: EMC STORAGE FOR PHYSICAL SECURITY DVTel Latitude NVMS performance using EMC Isilon storage arrays Correct sizing for storage in a DVTel Latitude physical security

More information

CSE 237A Final Project Final Report

CSE 237A Final Project Final Report CSE 237A Final Project Final Report Multi-way video conferencing system over 802.11 wireless network Motivation Yanhua Mao and Shan Yan The latest technology trends in personal mobile computing are towards

More information

ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN MEDICAL IMAGES

ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN MEDICAL IMAGES ISSN 392 24X INFORMATION TECHNOLOGY AND CONTROL, 2006, Vol.35, No.4 ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN MEDICAL IMAGES Darius Mateika, Romanas Martavičius Department of Electronic Systems,

More information

Keywords: Image complexity, PSNR, Levenberg-Marquardt, Multi-layer neural network.

Keywords: Image complexity, PSNR, Levenberg-Marquardt, Multi-layer neural network. Global Journal of Computer Science and Technology Volume 11 Issue 3 Version 1.0 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172

More information

The enhancement of the operating speed of the algorithm of adaptive compression of binary bitmap images

The enhancement of the operating speed of the algorithm of adaptive compression of binary bitmap images The enhancement of the operating speed of the algorithm of adaptive compression of binary bitmap images Borusyak A.V. Research Institute of Applied Mathematics and Cybernetics Lobachevsky Nizhni Novgorod

More information

http://www.springer.com/0-387-23402-0

http://www.springer.com/0-387-23402-0 http://www.springer.com/0-387-23402-0 Chapter 2 VISUAL DATA FORMATS 1. Image and Video Data Digital visual data is usually organised in rectangular arrays denoted as frames, the elements of these arrays

More information

Shared Desktop: A Collaborative Tool for Sharing 3-D Applications among Different Window Systems

Shared Desktop: A Collaborative Tool for Sharing 3-D Applications among Different Window Systems Shared Desktop: A Collaborative Tool for Sharing 3-D Applications among Different Window Systems Lawrence G. Palmer Ricky S. Palmer The DIGITAL Workstations Group has designed a software application that

More information

Using Keil software with Linux via VirtualBox

Using Keil software with Linux via VirtualBox Using Keil software with Linux via VirtualBox Introduction The Keil UVision software used to develop programs for ARM based microprocessor systems is designed to run on Microsoft Windows operating systems.

More information

Web Browsing Performance of Wireless Thin-client Computing

Web Browsing Performance of Wireless Thin-client Computing Web Browsing Performance of Wireless Thin-client Computing S. Jae Yang Dept. of Computer Science Columbia University New York, NY 27 sy8@columbia.edu Jason Nieh Dept. of Computer Science Columbia University

More information

Efficient Load Balancing using VM Migration by QEMU-KVM

Efficient Load Balancing using VM Migration by QEMU-KVM International Journal of Computer Science and Telecommunications [Volume 5, Issue 8, August 2014] 49 ISSN 2047-3338 Efficient Load Balancing using VM Migration by QEMU-KVM Sharang Telkikar 1, Shreyas Talele

More information

Video Coding Basics. Yao Wang Polytechnic University, Brooklyn, NY11201 yao@vision.poly.edu

Video Coding Basics. Yao Wang Polytechnic University, Brooklyn, NY11201 yao@vision.poly.edu Video Coding Basics Yao Wang Polytechnic University, Brooklyn, NY11201 yao@vision.poly.edu Outline Motivation for video coding Basic ideas in video coding Block diagram of a typical video codec Different

More information

BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 123 CHAPTER 7 BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 7.1 Introduction Even though using SVM presents

More information

Firefox, Opera, Safari for Windows BMP file handling information leak. September 2008. Discovered by: Mateusz j00ru Jurczyk, Hispasec Labs

Firefox, Opera, Safari for Windows BMP file handling information leak. September 2008. Discovered by: Mateusz j00ru Jurczyk, Hispasec Labs Firefox, Opera, Safari for Windows BMP file handling information leak September 2008 Discovered by: Mateusz j00ru Jurczyk, Hispasec Labs 1. Introduction The bitmap format implementations in Mozilla Firefox

More information

Video-Conferencing System

Video-Conferencing System Video-Conferencing System Evan Broder and C. Christoher Post Introductory Digital Systems Laboratory November 2, 2007 Abstract The goal of this project is to create a video/audio conferencing system. Video

More information

Remote Access and Control of the. Programmer/Controller. Version 1.0 9/07/05

Remote Access and Control of the. Programmer/Controller. Version 1.0 9/07/05 Remote Access and Control of the Programmer/Controller Version 1.0 9/07/05 Remote Access and Control... 3 Introduction... 3 Installing Remote Access Viewer... 4 System Requirements... 4 Activate Java console...

More information

Implementation and Analysis of Efficient Lossless Image Compression Algorithm

Implementation and Analysis of Efficient Lossless Image Compression Algorithm International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-6, June 2014 Implementation and Analysis of Efficient Lossless Image Compression Algorithm Prof.Megha

More information

Best Practices for Deploying Citrix XenDesktop on NexentaStor Open Storage

Best Practices for Deploying Citrix XenDesktop on NexentaStor Open Storage Best Practices for Deploying Citrix XenDesktop on NexentaStor Open Storage White Paper July, 2011 Deploying Citrix XenDesktop on NexentaStor Open Storage Table of Contents The Challenges of VDI Storage

More information

Figure 1: Relation between codec, data containers and compression algorithms.

Figure 1: Relation between codec, data containers and compression algorithms. Video Compression Djordje Mitrovic University of Edinburgh This document deals with the issues of video compression. The algorithm, which is used by the MPEG standards, will be elucidated upon in order

More information

An Implementation of a High Capacity 2D Barcode

An Implementation of a High Capacity 2D Barcode An Implementation of a High Capacity 2D Barcode Puchong Subpratatsavee 1 and Pramote Kuacharoen 2 Department of Computer Science, Graduate School of Applied Statistics National Institute of Development

More information

Arithmetic Coding: Introduction

Arithmetic Coding: Introduction Data Compression Arithmetic coding Arithmetic Coding: Introduction Allows using fractional parts of bits!! Used in PPM, JPEG/MPEG (as option), Bzip More time costly than Huffman, but integer implementation

More information

Conceptual Framework Strategies for Image Compression: A Review

Conceptual Framework Strategies for Image Compression: A Review International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Special Issue-1 E-ISSN: 2347-2693 Conceptual Framework Strategies for Image Compression: A Review Sumanta Lal

More information

A New Robust Algorithm for Video Text Extraction

A New Robust Algorithm for Video Text Extraction A New Robust Algorithm for Video Text Extraction Pattern Recognition, vol. 36, no. 6, June 2003 Edward K. Wong and Minya Chen School of Electrical Engineering and Computer Science Kyungpook National Univ.

More information

Building an efficient and inexpensive PACS system. OsiriX - dcm4chee - JPEG2000

Building an efficient and inexpensive PACS system. OsiriX - dcm4chee - JPEG2000 Building an efficient and inexpensive PACS system OsiriX - dcm4chee - JPEG2000 The latest version of OsiriX greatly improves compressed DICOM support, specifically JPEG2000 1 support. In this paper, we

More information

Study and Implementation of Video Compression Standards (H.264/AVC and Dirac)

Study and Implementation of Video Compression Standards (H.264/AVC and Dirac) Project Proposal Study and Implementation of Video Compression Standards (H.264/AVC and Dirac) Sumedha Phatak-1000731131- sumedha.phatak@mavs.uta.edu Objective: A study, implementation and comparison of

More information

CS1112 Spring 2014 Project 4. Objectives. 3 Pixelation for Identity Protection. due Thursday, 3/27, at 11pm

CS1112 Spring 2014 Project 4. Objectives. 3 Pixelation for Identity Protection. due Thursday, 3/27, at 11pm CS1112 Spring 2014 Project 4 due Thursday, 3/27, at 11pm You must work either on your own or with one partner. If you work with a partner you must first register as a group in CMS and then submit your

More information

SIGMOD RWE Review Towards Proximity Pattern Mining in Large Graphs

SIGMOD RWE Review Towards Proximity Pattern Mining in Large Graphs SIGMOD RWE Review Towards Proximity Pattern Mining in Large Graphs Fabian Hueske, TU Berlin June 26, 21 1 Review This document is a review report on the paper Towards Proximity Pattern Mining in Large

More information

QUANTITATIVE ANALYSIS OF IMAGE QUALITY OF LOSSY COMPRESSION IMAGES

QUANTITATIVE ANALYSIS OF IMAGE QUALITY OF LOSSY COMPRESSION IMAGES QUANTITATIVE ANALYSIS OF IMAGE QUALITY OF LOSSY COMPRESSION IMAGES Ryuji Matsuoka*, Mitsuo Sone, Kiyonari Fukue, Kohei Cho, Haruhisa Shimoda Tokai University Research & Information Center 2-28-4 Tomigaya,

More information

Dell Desktop Virtualization Solutions Stack with Teradici APEX 2800 server offload card

Dell Desktop Virtualization Solutions Stack with Teradici APEX 2800 server offload card Dell Desktop Virtualization Solutions Stack with Teradici APEX 2800 server offload card Performance Validation A joint Teradici / Dell white paper Contents 1. Executive overview...2 2. Introduction...3

More information

A Prediction-Based Transcoding System for Video Conference in Cloud Computing

A Prediction-Based Transcoding System for Video Conference in Cloud Computing A Prediction-Based Transcoding System for Video Conference in Cloud Computing Yongquan Chen 1 Abstract. We design a transcoding system that can provide dynamic transcoding services for various types of

More information

Product Description. Licenses Notice. Introduction TC-200

Product Description. Licenses Notice. Introduction TC-200 User Manual TC-200 Introduction TC-200 Product Description The TC-200 provides the fastest Thin Client performance on the market, It runs embedded Linux, swing user interface, Citrix 6.3, Microsoft RDP

More information