3 IMAGE COMPRESSION ALGORITHMS

Transcription

1 3 IMAGE COMPRESSION ALGORITHMS Despite the power of computers and communication systems which are on the increase every day, the need to store and transmit digital information in digital images and video overwhelm the capacity of many storage and communication facilities. This is because of the expansion that has been witnessed in the last few decades in the use of digital still and video images in various consumer, commercial and scientific applications, such as the advent of digital broadcast systems as in the HDTV and DSS, remote sensing, medical imaging, space exploration, video telephony, video conferencing and especially now the wide spread use of the internet and multimedia enabled PC s. and the large amount of information resulted that need to be stored or transmitted. For example an image of size 1024 pixel x 1024 pixel with 24 bits/pixel would require 25 Mb of storage and about 6.5 minutes to transmit using 64 k bit/s., ISDN line. If it can be compressed with 10:1 compression ratio, the storage required would be reduced to 2.5 Mb, and the transmission time will drop to 39 seconds. Consequently, image and video compression is still the current focus of many researchers in the field of image processing and also the focus of international standardization. The aim of image compression is to represent the reproduced image with a lesser number of bits than the original, while maintaining good quality in comparison to the original. This will facilitate the storage and transmission of images with less cost. Ideally no loss in quality would be desired, but this ultimately restricts the compression algorithms. In order to achieve compression in images, information from the original has to be removed. The two fundamental component of image compression are redundancy and irrelevancy [19]. In redundancy reduction; the statistically redundant data due to the correlation between the neighboring pixels and correlation between different frames in case of video images, can be removed from the data without destroying any information whatsoever [3]. The amount of compression achieved in this case is dependent on the amount of correlation present. In this kind of compression, the reconstructed image is exactly the same as the original since the data removed is redundant. This kind of compression is called lossless compression. Lossless compression can at best achieve only a modest amount of compression (about 2:1). Lossless compression is used in the application where data loss is usually not allowed and more accuracy is required, such as in medical imaging. In irrelevancy or subjective reduction, data can be removed from the image without complaint by the human observer as the final image receiver. Unlike statistical reduction, the removal of data in subjective reduction is irreversible [3]. The original data can not be recovered following the removal. This is termed lossy compression. Lossy compression is capable of achieving much higher 19

2 compression (20:1 and higher), and virtually all consumer orientated image and video applications rely on lossy compression to some extent [10]. Over the years many different algorithms for image compression has been developed [20,21,22,23,24,25,26,27]. In this chapter, some of the most common image compression algorithms are briefly described, also the most common standards will be mentioned. Some of the most common types of artifacts that resulted from compressing images are given at the end of this chapter. 3.1 Predictive Coding In Predictive coding[27], the spatial correlation between pixels in an image is exploited by predicting the value of the pixel from the previously coded pixels. The difference between the actual and the predicted values which is called the predicted error is quantized and transmitted [27]. Figure 3.1 shows the general block diagram of prediction coding technique. + - quantization Bit assignment Predictor + + Figure 3.1 General block diagram of predictive coding. One particular class of predictive coding which is widely used in image compression is known as Differential Pulse Code Modulation (DPCM). In DPCM the present data value is estimated based on previously encoded data [3]. The difference between the actual sample and the predicted sample (prediction error) is quantized and transmitted [3]. In its simplest case, where the prediction is based on the previous M quantized samples of the input image [28], for linear prediction: M x~ (n) = m= 1 a ~ m x (n-m) (3.1) 20

3 Where ~ x (n) is the predicted value of x (n), a m correspond to the prediction filter coefficients, and ~ x (n-m) are the previous quantized samples. The prediction error d (n) is given by: d (n) = x(n) - x ~ (n) (3.2) and the prediction gain is given by: G p = σ σ (3.3) 2 2 x / d Where σ x and σ d are the variances of the input signal and the prediction error signal, respectively. In the reconstruction process, the present sample is predicted from the previous samples using a predictor similar to the one used in the coding. Adding the prediction error to the predicted value result in the original image, and the process is lossless. The main disadvantage of DPCM is that, since the present sample is predicted from the previous samples, any error in a sample will propagate [11]. 3.2 Pyramidal Coding In the pyramidal coding [29], a set of successively smaller images are created by down-sampling (low-pass filtering and sub-sampling)[25]. First the original image is down-sampled to give low-pass version of the original image. The resulted low-pass image is up-sampled (zero placed between the retained samples) and subtracted from the original to give an error image. The error image is high-pass version of the original and its pixels are less correlated. The same process is repeated for the low-pass image, and a set of successively smaller images are created. The error images are transmitted. Since the propagation resembles a pyramid, this form of coding is called pyramidal coding. Expanding and summing all levels of the pyrimad, the original image can be recovered completely. 3.3 Transform Coding In transform coding [3,23,27,30,31], a block of correlated pixels is transformed into a set of less correlated coefficients. The transform usually linear and orthogonal. The transform to be used for data compression should satisfy two objectives. Firstly, it should provide energy compaction: i.e. the energy in the transform coefficients should be concentrated to as few coefficients as possible. This is referred to as the energy compaction property of the transform. Secondly, it should minimize the statistical correlation between the transform coefficients. 21

4 As consequence transform coding has a good capability of data compression, because not all transform coefficients need to be transmitted in order to obtain good image quality, and even those that are transmitted need not be represented with full accuracy in order to obtain good image quality. In addition the transform domain coefficients are generally related to the spatial frequencies in the image [28], and hence the compression techniques can exploit the psycho-visual properties of the HVS, by quantizing the higher frequency coefficients more coarsely, as the HVS is more sensitive to the lower frequency coefficients. In one-dimensional case, the transformation can be given by: C = T X (3.4) Where X is N x 1 block of data, C is N x 1 vector of transform coefficients, and T is N x N transform matrix or transformation kernels or basis functions. The original signal (image) can be reconstructed using the inverse transform given by: 1 X = T C (3.5) 1 Where T is the inverse of T For orthonormal (unitary) transform; t 1 T = T (3.6) Therefore, t X = T C (3.7) In the two dimensional transform, it is applied to a block of N x N data X, to give: And X = C = T X Where C is the coefficient matrix of size N x N. t T (3.8) t T CT (3.9) A simplified block diagram of image transform coding is shown in Figure 3.2. Source image Transform Quantization Entropy coding Figure 3.2 General block diagram of transform coding. coded image In general transform coding can be generalized into four stages: Image subdivision. Transformation. Quantization. 22

5 Entropy coding. Some of the transforms that have been utilized in image coding are given below Karhunen-Loeve Transform (KLT) The KLT [20,23,31], also known as Hotelling transform, is a linear transform, its basis functions are the eigenvectors of the image auto-correlation matrix. It is considered as the optimum transform in the sense of producing a set of uncorrelated coefficients from the correlated data, it s also results in the best energy compaction compared to other transforms. Altough KLT transform is considered an optimum transform, its not widely used in image coding, because knowledge of image statistics is always required, and because of its computational complexity Discrete Cosine Transform (DCT) The Discrete Cosine Transform [20,32,33,34] has become the most widely used unitary transform in image compression since its introduction in 1974 [32]. It has been shown to perform very closely to the KLT transform in producing uncorrelated coefficients, and in compacting the image energy in few transform coefficients. Another very important property of DCT is the ability to quantize the DCT coefficients according to the properties of the HVS [25]. Unlike the KLT, the DCT basis is image independent, which requires significantly lower complexity to determine the basis functions. Computation of the DCT can be performed using fast algorithms [25], which is desirable for both hardware and software implementation. All these features have made the DCT the standard method of image compression in many image-coding standard such as, JPEG, MPEG, and H.261 [25,26,35]. The two-dimensional DCT of an N x N data block is defined as: N 1 N 1 F (u, v) = 2/N C (u) C (v) [ x= 0 y= 0 f (x, y) cos (2x + 1) uπ 2N cos (2y + 1) vπ 2N] (3.10) Where, c(u), c(v) = 1/sqrt.of 2, for u and v =0, and c(u), c(v) = 1 otherwise. and f (x, y) are the data values and F (u,v) are the transform coefficients. 23

6 The inverse two-dimensional N x N DCT (IDCT) is defined as: N 1 N 1 f (x, y) = 2/N [ u= 0 v= 0 C(u) C(v) F(u,v) cos (2x +1)uπ 2N cos (2y + 1)vπ 2N ] (3.11) The DCT transformation kernels are separable, i.e. the two-dimensional transformation can be obtained by applying a one-dimensional DCT on the data block rows first then to the columns, or vice-versa. Typically two dimensional DCT is performed on 16 x 16 image blocks or 8 x 8 image blocks as in the case of JPEG. At a very low bit rates the DCT has its drawback in the appearance of blocking artifacts, which results from transforming discrete non-overlapping blocks, and the final samples on one block will not normally match with the first samples of the next block. The blocking artifacts can be reduced by the use of Lapped Orthogonal Transform (LOT)[36,37], in which overlapping blocks are processed, which takes the inter-block correlation into account [31]. 3.4 Subband coding In subband coding (SBC) [38,39,40,41], the image is split into a number of frequency bands called subbands,and then each subband is coded independently using coder matched to the statistics of that band. This is based on the fact that the HVS is sensitive to different bands of different frequencies differently. The SBC has two main advantages, namely: variable bit assignment among the subbands, and the coding error confinement within the subband [19]. The split of the image into subbands is obtained by using a group of band-pass filters [42], followed by critical sub-sampling. At the receiver the image is reconstructed by the reverse operation; up-sampling, and then the upsampled images are filtered using special filters called synthesis filters and summed to give the resultant image. Figure 3.3 shows the simplest form of SBC (2 band/channel system). 24

7 H0(w) 2:1 1:2 H F0 (w) X(n) Analysis filters down sampling encoding up-sampling synthesis filter H1 (w) 2:1 1: F1(w) Figure 3.3, A two-channel subband split. Two-band systems can be extended to a general M band system. Figure 3.4 shows a 4 band system obtained by cascading two stages of a 2 band decomposition. H0(w) 2:1 H0(w) 2:1 H1(W) 2:1 X (n) H0(w) 2:1 H1(w) 2:1 H1(w) 2:1 Figure band subband system. In the application of subband coding to images 4-band separable split is the simplest form of decomposition used. The pyramidal coding mention in section 3.2. can be considered as special case of subband coding. 25

8 3.5 Wavelet-based coding Over the past several years, the wavelet has gained a widespread use in the field of signal processing in general and in image compression in particular[43,44,45,46,47,48,49]. It is a mathematical transform that perform a multi-resolution decomposition of an image[46]. Wavelet is a subset of subband coding. It splits the image into subbands which are located in the space-frequency domain. Wavelet transform have many advantages over the blocking transforms, such as the DCT. In wavelet coding the input image is not divided into blocks, so blocking artifacts which are very annoying to the human observer are avoided in wavelet-based schemes at higher compression. Wavelet-based coding is however more robust under transmission and decoding errors. Wavelet-based coding also facilitates progressive transmission. Very high compression ratios can be achieved whilst maintaining important data, such as sharp changes in contrast which would represent edges. The wavelet transform is to replace blocking transforms in many coding standards, such as the replacement of DCT in the JPEG standard by a wavelet transform in the new JPEG-2000, which was expected to be finalized by the end of the year 2000 [50,63]. A variety of wavelet-based coding schemes have been developed over the past few years [48,49,51,52,53]. 3.5 Quantization Most of the transforms used for image compression are lossless, as the original image can be reconstructed almost exactly from the transformed image. In order to achieve compression the transformed coefficients should be quantized. A quantizer simply reduces the number of bits needed to store the transformed coefficients by reducing the precision of those values. Quantization is a many to one mapping, so it s a lossy process and is the main source of compression in the encoder. Quantizers can be classified as; either scalar or vector quantizers. Scalar quantization is performed on each individual coefficient independently. Vector quantization (VQ) is performed on a group of coefficients together [54]. In all types of quantization, the difference between the unquantized input and the quantized output is called the quantization error (or noise), it is desirable to minimize the perceived magnitude of this error. 26

9 Scalar Quantization For a signal x with a continuous amplitude specified by an index k given by: I : < k xk x <, k= 1,2,..L (3.12) xk + 1 The quantizer will represent the signal with a finite number of levels: y { y 1, y 2, y l ), such that : y = y k if x Ik are called the decision levels and y xk l are called the reconstruction levels. In the quantizer where the zero is one of the reconstruction levels,the quantizer is called a mid-tread type quantizer,othewise it is called mid-riser type quantizer [27]. Figure 3.5, show the two types of quantizers. The quantizer also can be classified as uniform or non-uniform, depending on the choice of decision and reconstruction leveles. If the decision levels I are of the same width, the k quantizer is classified as uniform quantizer [3]. If the decision levels I vary k depending on the input amplitude, the quantizer is classified as a non-uniform quantizer. Figure 3.6 shows examples of a uniform and a non-uniform quantizers. Quantizers also can be classified as an adaptive quantizer if the decision and reconstruction leveles are based on some property of the input signal. Output Output Input Input Mid-riser Quantizer Mid-tread Quantizer Figure 3.5 Mid-tread and mid-riser quantizers. 27

10 Output Output Input Input Uniform quantizer Nonuniform quantizer Vector Quantization Figure 3.6 Uniform and Nonuniform quantizer. In Vector Quantization (VQ), quantization is performed on a group of coefficients together. i.e. a group of samples (a vector) is quantized to a single representative value [54]. VQ uses a codebook containing pixel patterns with a corresponding index for each of them, and the idea is to represent arrays of pixels by an index in the codebook. Compression is achieved since the size of the index is usually a small fraction of that of the block of pixels. VQ is an efficient method for image compression and good quality can be gained with large vectors. However in order to use large vectors the complexity will increase and this is what has limited the use of VQ in image coding so far. 3.6 Entropy Coding The quantized coefficients are further compressed losslessly using entropy encoder [55,56]. These encoders work on the probabilities of the quantized values and produce an appropriate code based on these probabilities, so the output code stream will be smaller than the input stream.the most commonly used entropy coders is: the Huffman encoder [57],where higher compression ratios are achieved by assigning a shorter length codewords to the more probable levels, and a longer length codewords to the less probable levels. Another important 28

11 encoder used is the arithmetic encoder. Also a simple run-length encoding (RLE) is used in image compression. 3.7 Compression Standards Over the past decade or so a number of standards have been defined for the compression of visual information, Table 3.1, summarizes the major image and video standards available to date. The compression standards have provided a mechanism for interoperability between different applications which utilize compression [10]. Compression standards provide a means for controlling image quality. The most common up to date, still image compression standard is the JPEG standard, which is discussed in the following sub-section. Standard Standardization body Main Target bitrate Main compression technology Main Target applications JPEG ISO/IEC Compression ratio 2-30 DCT, Perceptual Quantization. Zig-zag reordering, Huffman coding, Arithmetic coding. Internet imaging, Digital photo. Image and video Editing. JPEG2000 ISO/IEC Compression ratios 2-50 MPEG-1 ISO/IEC Bitrates up to about 1.5 Mb/s MPEG-2 ISO/IEC Bitrates 1.5 Mb/s To 35 Mb/s To be defined DCT, perceptual Quantization, Adaptive quantization, zig-zag reordering, predictive motion compensation, Bidirectional motion compensation, Half-sample accuracy, motion compensation, Huffman coding, Arithmetic coding. DCT, perceptual Quantization, Adaptive quantization, ziz-zag Reordering, predictive motion Compensation, Bi-directional motion compensation, frame/ field based motion compensation,half sample accuracy, motion compensation, spatial scale ability, temporal scalability, quality scalability, Huffman coding,arithmetic Internet imaging, Digital photo. Image and video Editing,printing, Medical imaging, Mobile applications Color fax, Satellite imaging. Storage on CD- ROM,Consumer Video. Digital TV,Digital HDTV,High Quality video, Satellite TV, Cable TV, Terristrial broadcast, Video editing, Video storage. 29

12 MPEG-4 ISO/IEC Bitrates 8 kb/s to About 35 Mb/s coding,error Resilient coding. DCT,Wavelet, Perceptual Internet, Interactive Quantization, adaptive Video, visual editing, content manip- Quantization, zig-zag Reordering, zero-tree reordering ulation, consumer predictive motion compensation, video, professional Bi-directional motion video, 2D/3D computer graphics, compensation, frame/field based motion compensation, advanced mobile. motion estimation, overlapping motion compensation, spatial scalability, temporal scalability quality scalability, view dependent scalability, bitmap shape coding, sprite coding, face animation, dynamic mesh coding, Huffman coding, Arithmetic coding, Error resilient coding. MPEG-7 ISO/IEC To be defined To be defined Visual content Search. H.261 ITU-T Bitrates p x 64 kb/s (p : 1-31) DCT, adaptive quantization, zig-zag reordering, predictive motion compensation, integer-sample accuracy motion estimation, Huffman coding, ISDN video conferencing. H.263 ITU-T Bitrates 8 kb/s up to 1.5 Mb/s Error resilient coding. DCT, adaptive quantization, zig-zag reordering, predictive motion compensation, Bi-directional motion compensation, Half-sample accuracy motion Estimation, advanced motion estimation, overlapping motion compensation, Huffman coding, Arithmetic coding, Error resilient coding. POTS video-tele- Phony, Desktop video telephony, Mobile video telephony. Table 3.1, Summary of present and emerging standards for coding and representation of visual information, from [58] JPEG Image compression standard JPEG is an acronym for Joint Photographic Expert Group [59]. A group working under the international organization for standardization (ISO), the international telegraph and telephone consultative committee (CCITT) and the 30

13 international electrotechnical commission (IEC). JPEG is the first international standard for compression of and coding of continue-tone still images. It was approved in 1991, JPEG was not intended for lettering, cartoons, or line drawing, for which another standard exists: Joint Bi-level Image Expert Group (JBIG). JPEG has a number of modes of operation [60]: Baseline (sequential) encoding: where each image component is encoded in a single Left-to-right, top-to-bottom scan. It is a DCT-based mode of operation. Progressive encoding; The image is encoded in a multiple scans for applications in which transmission time is long, and the viewer prefers to watch the image build up in a multiple coarse-to-clear passes. It is also DCT-based mode. Lossless encoding; the image is encoded in such a way to recover the source image exactly. It is based on the predictive coding. Hierarchical encoding; the image is encoded at multiple resolution, so that lower resolution versions may be accessed without first having to decompress the image at its full resolution. The most commonly implemented JPEG mode is the baseline (sequential) algorithm. Figure 3.7, shows the key processing steps which are the heart of the DCT-based modes of operation. All other JPEG modes are extension of the baseline mode, and retain many of the baseline features [60]. 8x8 blocks DCT-Based Encoder FDCT Quantizer Entropy Encoder Compressed image data Source image Data Table specification Table specification Figure 3.7 JPEG, DCT-Based Encoder 31

14 DCT-Based Decoder Compressed Image data Entropy Decoder Dequantizer IDCT Reconstructed Image data Table Specification Table Specification Figure 3.8 JPEG, DCT-Based Decoder In the sequential mode of operation; for the gray scale images; the following steps are followed: At the input to the encoder, the source image samples are divided into 8x8 nonoverlapping blocks, and the samples shifted from unsigned integers with range ( 0 2 p -1),to a signed integers with range ( -2 p 1 2 p 1-1 ), where p is the number of bits represent the input data samples. Forward DCT is performed on each block by applying the FDCT equation on each 8x8 data block to get the DCT coefficients; F (u,v) = ¼ C( u ) C( v ) [ = 7 x 0 7 y= 0 C(u), C(v) = 1 2 for u, v = 0. = 1 otherwise. f(x,y) cos(2x +1)uπ 16 cos(2y+1)vπ 16] (3.13) This operation will produce 64 coefficients for each 8x8 data blocks. From F(0,0), to F (7,7). F(0,0) is called the DC coefficient, and the rest are the AC coefficients. Most of the energy is concentrated in few lower frequency coefficients. Most of the high spatial frequency coefficients have zero or near zero amplitudes. Each of the 64 DCT coefficients are uniformly quantized using one of 64 corresponding values from a quantization table(matrix). No default values for quantization tables are specified in the JPEG specification [25]. It is left to the application to specify values according to their picture quality required for their 32

15 particular image characteristic, display device, and viewing conditions. An example of quantization tables for both luminance and chrominance components are given in the JPEG specifications, and are found to be suitable for many applications [25].The quality and bit rate of an encoded image can be varied by changing the entries of the quantization table. The quantization table may be designed according to the perceptual importance of the DCT coefficients under the specified viewing conditions, i.e. the HVS contrast sensitivity function can be used to determine the entries of the quantization table. Quantization is the main source of loss in the sequential mode of operation, therefore it is the source of degradation in the reconstructed images. Quantization is defined as division of each DCT coefficient by its corresponding quantizer step size and rounding to the nearest integer: F ( u, v) = integer round ( Q Q( u, v) where F (u, v) is the quantized coefficient, Q F ( u, v) ) (3.14) F(u, v) is the DCT coefficient before quantization Q(u, v) is the corresponding entry in the quantization matrix. At the decoder the inverse quantization is performed as: / F (u, v) = Q F (u, v) Q(u, v) (3.15) Q The quantization matrix is part of the information must be supplied to the decoder. After quantization the DC coefficient which contains a significant fraction of the total image energy; it is a measure of the average value of the 64 image samples. This DC coefficient is differentially encoded, taking advantage of the normally strong correlation between the DC coefficients of the adjacent 8x8 blocks. Therefore the quantized DC coefficient is encoded as the difference from the DC coefficient of the previous block in the encoding order [60], as shown in Figure 3.9, (a). After quantization of all the DCT coefficients, they are ordered into a zig-zag sequence as shown in Figure 3.9, (b). This ordering helps to facilitate the next step; entropy coding: by placing low frequency coefficients, which are more likely to be non-zero, before the high frequency coefficients, which are more likely to be zero after quantization. 33

16 DCi-1 DCi block i 1 block i DIFF = DCi - DCi (a) Figure 3.9, Preparation of Quantized Coefficients for Entropy Coding, (a) Differential DC encoding, (b) Zig-zag ordering. (b) The quantization and zig-zag ordering will produce a large run of zero coefficients at the end of the scan. The run-length encoding: where the run/value combinations are mapped into codewords; will be employed to take advantage of the large run of zeros and lossless compression can be achieved. Further lossless compression can be achieved by employing a variable length coding [61], such as, Huffman coding [57], or arithmetic coding [25], the base-line JPEG uses Huffman coding [60]. Huffman coding achieves a reduction in the average number of bits per codeword, by assigning shorter codes to codewords having high probability of occurrence, and longer codes to codewords having lower probability of occurrence [3]. The decoding process is just the inverse of the coding process. Figure 3.8 shows the DCT-based jpeg decoder processing steps. Figure 3.10 shows some example images compressed using JPEG baseline for different scaling of the quantization table. The scaling is obtained by using a Q (quality) factor based on the independent JPEG group (IJP), JPEG implementation [62]. The Q factor ranges from 1 to 100, where 100 means no quantization. and the produced image should be identical to the original. Lower Q factor means higher compression ratio, but poorer image quality. The quantization matrix used here is the example quantization matrix given with the JPEG specifications. 34

17 3.8 Coding Artifacts Introduced by Digital Image Compression Lossy image compression algorithms introduce a number of different kinds of artifacts into the produced images. these artifacts are introduced mainly at the quantization stage. These artifacts/degradations have different effects on the human perception of images [10], depending on many factors, such as; the artifact s magnitude, spatial and temporal persistence, location and structure. In the following subsections some common types of coding artifacts and their causes are described Blocking Blocking (or tiling) artifacts are a typical artifacts characterized by the appearance of discontinuities at the boundaries of adjacent blocks. Blocking artifacts occurs in coding schemes that use a discrete (non-overlapped) block processing, such as, JPEG, MPEG, and VQ. It is usually caused by quantizing the lower DCT coefficients coarsely Granular Noise Produced by DPCM, granular noise structure appears in the flat areas of the picture as a result of coarse quantization at low detailed areas of the picture Slope Overload Slope overload is also associated with DPCM, and it occurs if the largest possible representation of the quantizer is too small; a high contrast edge with a high gradient will not be followed accurately which result in a slope overload. The result of this is blurring the high contrast edges [11] Edge Business Distortion concentrated at or near the edges of objects in a picture, results in edge business. This is also a DPCM artifact, and occurs when the quantizer output oscillates around the signal value when the slope changes gradually near the edges Blurring Blurring is characterized by reduced sharpness of edges and spatial details. It is caused by the elimination of high frequency coefficients. Blurring can be found in most lossy compression schemes. 35

18 3.8.6 Error blocks Error blocks are characterized by one or more blocks in an image contrast greatly with adjacent blocks. It occurs in block-based schemes that undergo channel errors Ringing Ringing is more pronounced at image edges, where it appears as a series of edges parallel to the original edges at which it occurs, and decays gradually away from the edges [11]. It is common to most coding schemes, that attempt to reduce blocking, such as, subband, LOT, and wavelet coding. Some of the above mentioned artifacts are shown in Figure Plus all the above mentioned artifacts, there are many other kind of artifacts which effect video coding, such as Jerkiness, where the originally smooth and continuous motion will be received as a series of a distinct snapshots. This is introduced by video coders that uses temporal sampling or interpolation. Mosquito noise, characterized by moving artifacts superimposed over objects in a scene. It is a consequence of the varied coding of the same area of a scene in consecutive frames in a video. Object persistence distortions, where objects that should no longer appear, remain in subsequent frames as an outline or faded images. It is a result of temporal interpolation being carried out over too many frames. 3.8 Summary In this chapter some of the state of the art image coding schemes used for image compression, such as, Predictive coding, Pyramidal coding, Transform coding, Subband coding, and Wavelet coding, were discussed. Quantization step is where compression is obtained, also discussed together with the different types of quantizers that can be used in image coding. Summary of the state of the art of compression standard was given, with more detailed explanation of the JPEG standard. In the final section of this chapter, some of the different types of artifacts that are introduced by the coding schemes to the reconstructed images were discussed. The following chapter will present the distortion measures for picture quality prediction. 36

19 Figure 3.11 (left to right, top to bottom), Original Lena image, Lena image with blocking artifacts (as a result of coarse quantization of the 8x8 DC-coefficients), Lena image with ringing artifacts (as a result of coarse quantization of the wavelet coefficients), Lena image with blurring artifacts (as a result of setting the high frequency DCT-coefficients to zeros). 37