A VIDEO COMPRESSION TECHNIQUE UTILIZING SPATIO-TEMPORAL LOWER COEFFICIENTS

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1 International Journal of Electronics and Communication Engineering & Technology (IJECET) Volume 7, Issue 1, Jan-Feb 2016, pp , Article ID: IJECET_07_01_002 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication A VIDEO COMPRESSION TECHNIQUE UTILIZING SPATIO-TEMPORAL LOWER COEFFICIENTS Ashwini Atulkar Department of Electronics and Communication Engineering, SATI Engineering College, Vidisha, M.P Abhishek Jain Asst. Professor, Department of Electronics & Communication Engineering SATI Engineering College, Vidisha, M.P ABSTRACT With the advancement of communication in recent trends, video compression plays an important role in the transmission of information on social networking and for storage with limited memory capacity. Also the inadequate bandwidth for transmission and lower quality make video compression a serious phenomenon to consider in the field of communication. There is a need to improve the video compression process which can encode the video data with low computational complexity with better quality along with maintaining speed. In this work, a new technique is developed based on the block processing utilizing the lower coefficients between frames. The performance parameters such as Compression Ratio (CR) & Peak Signal to Noise Ratio (PSNR) are evaluated based on the algorithm for several standard video sequences. The proposed method results in higher compression rates as compared to the spatial block based compression technique. The simulation result shows that the proposed algorithm sustain faithful compression and reproduction of the video, preserving the picture quality. Key words: Video Compression, Block Processing, Spatial. Cite this Article: AshwiniAtulkar and Abhishek Jain. A Video Compression Technique Utilizing Spatio-Temporal Lower Coefficients. International Journal of Electronics and Communication Engineering & Technology, 7(1), 2016, pp

2 A Video Compression Technique Utilizing Spatio-Temporal Lower Coefficients 1. INTRODUCTION The high requirement of bandwidth has been the major problem in a video. A typical system needs to send several frames per unit second so that the illusion of a moving picture can be generated. For instance, a colour video clip recorded with a resolution of 1920x1080 assuming 30 frames per second requires memory of 1.5 GB for second. This means even a DVD of capacity 5 GB cannot handle a video of 4 minutes duration. It is exactly for this reason video compression has become very significant. To eliminate the redundancy, each individual frame is coded. Also with motion compensation, a great amount of redundancy can be eliminated between two successive frames. In spite of the continuous development in the storage space and transmission bandwidth now-a-days, as the amount of data obtained by digitizing the video signal is huge, compression is very important in most of the digital video applications. A video is nothing but a sequence of images which are displayed one at a time. The continuous image sequence bluffs us that something is moving. There are very small changes in the video frames, which make one frame differ from the other. So all this makes one requirement of video compression as storage and transfer get easier. The compression of video tends two type of technique first is lossy compression where compression of video frame is done in a way that the original information is irreversible. Although this technique makes lots of space in disks and bandwidth, but due to loss of information it is undesirable. The other method is lossless in which original information can be successfully recovered after applying decompression technique. This paper has also developed similar kind of lossless technique. In compression method those information which is getting repeated in every frame is removed from the further frame [1, 2]. This identification of frame position and storage of that information is done in the video compression method. This is also categorized in inter and intra frame information compression where consecutive frames are used for finding similar data. While in case of intra-frame fault tolerance capacity increases as if one of the consecutive frames lost then video can be built-up with other frames available in the compressed one. Video is categorized in two type first is black and white while another is color. In case of black and white, gray format is used while in the case of color RGB color format is used in the frame. Now compression technique in the video is classified on the basis of spatial as well as on temporal way. Here in the case of spatial, pixel value of a frame is identified by the pixel value of the same frame. While in the case of the temporal, pixel value of a frame is identified by the pixel value of the other co-related frame. 2. RELATED WORK Liyin et.al [3] presented the Full Search method, which is the most computationally and more costly for all algorithm of block matching. By this algorithm, at each possible location in the search window calculates the cost function and gives the highest PSNR amongst any block matching algorithm and it finds the best possible match. To attain the same PSNR doing as little computation as possible by Fast block matching algorithms. Variable Size Block Matching Methods Attaining, good video compression ratio and image quality are a conflicting requirement, specially with fixed size block matching (FSBM), where the size of all the blocks is the same. The above problem can be overcome by variable size block matching motion estimation technique (VSBME). In variable-size block matching (VSBM) [4]- [5] smaller blocks 11

3 Ashwini Atulkar and Abhishek Jain can be used to depict complex motion while larger blocks can be used in areas where the image content is stationary or undergoing uniform motion while the no of blocks and bit rate are fixed. Quad tree-structured Variable-Size Block-Matching Motion Estimation [6][7] presents two algorithms with Minimal Error. The first algorithm computes the optimal selection of variable-sized blocks to provide the best achievable prediction error under the fixed number of blocks for a Quad tree-based VSBM technique. Although this algorithm is computationally intensive, it does provide a yardstick by which the performance of other more practical VSBM techniques can be measured. The video signal is an integral part of multimedia which has a tremendous importance in most of the applications involving the concept of the multimedia i.e. video conferencing, broadcast digital video, and high- definition television (HDTV), etc.[8]. Mueller introduces the generalized Gaussian distribution to model the DCTcoefficients more accurate than with Laplace distributions [9]. Mathematical morphology can be considered as a shape oriented approach to signal processing and some of its features make it very useful for compression for videos [10]. Man et.al proposed a novel Four-Step Search Algorithm for Fast Block Motion Estimation. The proposed algorithm is based on the center-based global minimum motion vector distribution characteristic of real world image sequence, a new Four-Step Search algorithm for fast block-based motion estimation [11]. A fast block- matching algorithm that uses three fast matching error measures, besides the conventional mean-absolute error (MAE) or mean-square error (MSE) is provided in [12]. Shen and Edward [13] proposed video compression using Based Rate Scalable Method; it is a new wavelet based rate scalable video compression algorithm. This technique is referred as the Scalable Adaptive Motion Compensated Wavelet (SAMCoW) algorithm. Edmund and Joseph proposed the distribution of DCT-coefficients in the field of image compression and an approximation of the AC-coefficients with Laplace distributions [14]. H.R. Kusuma discussed a comparison of various spatial and temporal redundancy removal techniques used for video compression[15]. The performance of Discrete Cosine Transform (DCT) based algorithm for compressing video with perceiving quality and content of video presented in [16]. M. Atheeshwari et.al.[17] presented compression techniques necessary for video processing especially to find how much amount of data to compressed. 3. PROPOSED WORK The proposed work is based on basic block processing spatial technique as well as Inter-frame video compression technique. In this methodology, the video compression is done by using a DCT block processing with a 6 coefficient mask by detecting and eliminating the similarities between the consecutive frames. The schematic diagram of the proposed methodology is shown in Fig.3 & Fig.4 and the major blocks are as follows: Video Reader Video is a collection of images which is called as a frame. Here these collections of images are displayed in so fast sequence that human eyes could not judge that they actually see one image at a time. As the contents of the consecutive frames are mostly same, but change in object position is new information on the frame. So a reading of video means conversion of video in a sequence of frames of RGB format

4 A Video Compression Technique Utilizing Spatio-Temporal Lower Coefficients Consecutive Frames Here frames are passed in pair of two, where the pairing of frames is done on the basis of their sequence. This sequence of frames is consecutive. So two consecutive frames are passed for each run. This can be understood as let V be video then this contain frames as {f1, f2, f3, fn}. So a pair of consecutive frames is (f1, f2), (f2, f3), (f3, f4), etc. Frames Figure 1 Collection of video frame 16x16 DCT Block Processing with 6 coefficient Mask In this step DCT block processing is done, which simply means that the frames of the video is divided into blocks of and then apply DCT on each block. Now the mask of 6 6 coefficient is applied to each block. By multiplying the DCT matrix by mask the elements of the matrix become zero, due to this the information get reduced. This is shown in Fig. 2. IDCT Processing In this step simple inverse process of DCT is done. Here each 16x16 block is recombine to make single frame. Euclidean distance Now the difference between the consecutive frames is evaluated in this step. This distance helps in identifying the frame similarity. As difference pixel is zero means those pixel position is unchanged in both frames. This can be understood as Let X be a frame and Y be the other consecutive frame from same video then distance can be calculated as: Sparse Matrix Here Non zero position in the distance matrix need to be saved in a matrix for future reference, when video need to be read. So sparse matrix is created for the frame which store non matching pixel value of the frames 13

5 Ashwini Atulkar and Abhishek Jain Single frame 16x16 blocks 6x6 Masking Figure 2 16x16 block DCT application Here one check is applied which helps in identifying that either the new matrix size is more or less as compared to the previous one. If the size of a matrix is less, than it is stored in sparse matrix otherwise save it as an original frame matrix. As it is already known that sparse matrix contain three columns first represent row, second represent column and third represent value at that position

6 A Video Compression Technique Utilizing Spatio-Temporal Lower Coefficients Read Video Consecutive Frames 16x16 DCT Block Processing with 16 Coefficient Mask IDCT Block Processing Euclidean distance Sparse Matrix Combine Frames Figure 3 Block diagram of proposed compression model Combine Frame In this step, all matrices of the frame combine in one cell and use store in the disk. This combination of matrix is of different size as different frames may have their different size of the matrix. Reconstruct Frame In case of de-compression, reverse process is required where each matrix of frames is reconstructed with the help of previous frame information. This can be understood as if frame is constructed from the sparse matrix, then it provides pixel value of the information stored in sparse matrix, then rest of pixel value is obtained from the previous frame. Read Video Consecutive Frames Reconstructed Frames Figure 4 Block diagram of proposed De-Compression model Proposed Compression Algorithm Input: V // V: Input Video Output: CV // CV: Compressed Video Combine Frame 1. V Read_Video(V) 2. FF[n] Consecutive_Frames(V) // FF[n]: n consecutive frame 3. Loop 1:n 4. B Blocks(FF[n]) 5. B Mask_DCT(B)\ 15

7 Ashwini Atulkar and Abhishek Jain 6. FF[n] IDCT(B) 7. D Euclidean_dist(FF[n]) // D is distance matrix 8. Loop 1:x // x, y are row and column of video frame 9. Loop 1:y 10. If D[x, y]) > SM[m] [x, y, D[x,y]) // m number of positions 12. EndIf 13. EndLoop 14. EndLoop 15. EndLoop 4. EXPERIMENT AND RESULT The proposed methodology is implemented for video compression and the results are compared with spatial method. Experimental results validate the effectiveness of the approach. All algorithms and utility measures were implemented using the MATLAB tool. The tests were performed on a 2.27 GHz Intel Core i3 machine, equipped with 4 GB of RAM, and running under Windows 7 Professional. Evaluation Parameters Peak Signal to Noise Ratio (PSNR) It is used to find the amount of data present from the received signal as it may corrupt by the presence of some noise. So it is termed as the peak signal to noise ratio. The ratio between the maximum possible data and the noise that affects the fidelity of its representation. Compression Ratio (CR) In this parameter storage size of the original video is compare with the video obtain after applying compression technique.in other word, the compression ratio is the ratio between uncompressed video size and compressed video size. Where, C.R. = Compression Ratio Data Sets An experiment done on the standard video name crocus, snowflake, etc. as well as on the artificial videos, Results technique: (a) (b) 16

8 A Video Compression Technique Utilizing Spatio-Temporal Lower Coefficients (c) (d) Figure 5 Original Video (a) (b) (c) (d) Figure 6 Compressed Video Table 1 Average value of PSNR for different standard videos PSNR Comparison Video Spatial Proposed Work Crocus Snowflake Fp Star Table.2 Average value of Compression Ratio for different standard videos Compression Ratio Video Spatial Proposed Work Crocus Snowflake Fp Star It has been observed by Table 1 & 2 that video compression by proposed work is better as compared to previous one, as PSNR value is higher. It is observed that as the number of the frames increases, then there is a chance of getting less PSNR value. 5. CONCLUSIONS Large number of video compression technique developed for different requirement. Proposed method utilizes the basic block processing combined with temporal lower coefficient compression method. It has been obtained that the proposed work is better as compared to the spatial method on the basis of different performance parameters. It is also obtained that video size affects the parameters like compression ratio, PSNR. If size of a video is small better compression ratio is obtained. As the size of a video is increased, compression ratio gets decreased. As there are various possibilities still 17

9 Ashwini Atulkar and Abhishek Jain present in the work like compression of grey video, inter-frame information compression etc. REFERENCES [1] T.A. Browne, J.V. Condell, G. Prasad, T.M. McGinnity, An Investigation into Optical Flow Computation on FPGA Hardware, International Machine Vision and Image Processing Conference, 2008, [2] FransciscoBarranco, MatteoTomasi, JavierDiaz, Mauricio Vanegas and Eduardo Ros Parallel Architecture for Hierarchical optical Flow Estimation based On FPGA, IEEE Transactions On very large integrated VLSI System, Volume 20, Issue 6, June 2012, [3] XieLiyin, Su Xiuqin, Zhang Shun, A Review of Motion Estimation Algorithms for Video Compression, International Conference on Computer Application and System Modeling (ICCASM 2010), [4] H. Jelveh and A. Nandi, Improved variable size block matching motion compensation for video conferencing applications, in Digital Signal Processing, A. Cappellini and A. Constantinides, Eds. Berlin, Germany: Springer-Verlag, [5] G.R. Martin, R.A. Packwood and I. Rhee, Variable Size Block Matching Motion Estimation with Minimal Error, Department of Computer Science, University of Warwick, Oct95. [6] I. Rhee, G. R. Martin, S. Muthukrishnan and R. A. Packwood, Quad tree- Structured Variable-Size Block- Matching Motion Estimation with Minimal Error, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 10,pp , Feb [7] Xiao-lin Chen, Design of UAV Video Compression System Based on H.264 Encoding Algorithm, International Conference on Electronic & Mechanical Engineering and Information Technolo-gy.Vol-5, pp , [8] C. Pandey, S. Kumar and R. Tiwari, An Innovative Approach towards the Video Compression Methodology of the H.264 Codec: using SPIHT Algorithms, International Journal of Soft Computing and Engineering (IJSCE) ISSN: , vol.2, Issue-5, November [9] F. Mueller, Distribution Shape of Two-Dimensional DCT Coefficients of Natural Images, Electronic Letters, vol. 29, pp , [10] P. Salembier, P.Brigger, J.R.Casas and M.Pardas, Morphological Operators for Image and Video Compression, IEEE Transactions on Image Processing, vol. 5, June [11] Lai-Man Po and Wing-Chung Ma, A Novel Four-Step Search Algorithm for Fast Block Motion Estimation, IEEE Trans. Circuits and Systems for Video Technology, vol. 6, June [12] Y.C. Lin and S.C. Tai, Fast Full-Search Block-Matching Algorithm for Motion- Compensated Video Compression, IEEE, vol. 45, [13] K.Shen and E.J. Delp, Wavelet Based Rate Scalable Video Compression, IEEE transactions on circuits and systems for video technology, vol. 9, [14] R. Reininger and J. Gibson, Distributions of the Two Dimensional DCT Coefficients for Images, IEEE Transactions Communication, vol. 31, pp , June [15] H. R. Kusuma and M. rao, Video Compression Using Spatial and Temporal Redundancy-A Comparative Study, International Journal of Innovative Research in Science, Engineering and Technology, vol. 4,pp

10 A Video Compression Technique Utilizing Spatio-Temporal Lower Coefficients [16] S. S. Wadd, B. S. Patil, Video Compression Using DCT, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 9, September [17] M.Atheeshwari, K.Mahesh, Video Compression Techniques A Comprehensive Survey, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 1, January [18] B.K.N.Srinivasa Rao, P.Sowmya. Architectural Implementation of Video Compression through Wavelet Transform Coding and EZW Coding. International Journal of Electronics and Communication Engineering & Technology, 3(3), 2012, pp [19] Dr. Uma B.V, Dr. Geetha K.S, Dr. Prasanna Kumar S.C and Naveen Kumar L. Implementation of H.264 Based Real Time Video Compression Algorithm for Underwater Channel. International Journal of Electronics and Communication Engineering & Technology, 5(4), 2014, pp

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