A Scalable Video Compression Algorithm for Real-time Internet Applications

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1 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications A Scalable Video Compression Algorithm for Real-time Internet Applications Mathias Johanson Abstract-- Ubiquitous use of real-time video communication on the Internet requires adaptive applications that can provide different levels of quality depending on the amount of resources available. For video coding this means that the algorithms must be designed to be scalable in terms of bandwidth, processing requirements and quality of the reconstructed signal. This paper presents a novel video compression and coding algorithm targeted at delay-sensitive applications in heterogeneous network and computing environments. The algorithm, based on the embedded zerotree wavelet algorithm for still image compression, generates a highly scalable layered bitstream that can be decoded at different qualities in terms of spatial resolution, frame rate and compression distortion. Furthermore, the algorithm is designed to require only a minimal coding delay, making it suitable for highly interactive communication applications like videoconferencing. The performance of the proposed algorithm is evaluated by comparison with a nonscalable codec and the penalty in compression efficiency that the scalability requirement imposes is analyzed. The codec is shown to produce a scalable bitstream ranging from about kbps to Mbps, while the computational complexity is kept at a level that makes software implementation on CPU-constrained equipment feasible. Index Terms Video compression, video conferencing, layered video, Wavelet. I. INTRODUCTION HE evolution of the Internet has enabled a new class of Tsynchronous multimedia communication applications with high demands on delay and bandwidth. Not only does this affect network and transport protocols, but also has a profound impact on the design of media encoding and compression algorithms. For teleconferencing applications the coding delay must be kept at a minimum while a high compression performance is maintained to efficiently utilize the available bandwidth. For videoconferencing this is of particular importance due to the high bandwidth and complexity imposed by video transmission and processing. Furthermore, since the Internet is a highly heterogeneous environment, both in terms of link capacity and end-equipment, video codecs need to be able to generate bitstreams that are highly scalable in terms of bandwidth and processing requirements. As the current Internet provides only a single class of service, without guarantees on bandwidth or lossrate, the applications need to be adaptive to variations in throughput and loss probability. The dissimilar requirements imposed by different applications and the heterogeneity problems have given birth to a multitude of video compression algorithms with different target bitrates, complexities M. Johanson is with Alkit Communications, Sallarängsbacken, S Mölndal, Sweden. and qualities. Multipoint videoconferences, where the participants in the general case are subject to different bandwidth constraints, can be realized using transcoding gateways that re-codes the video to different bandwidths. This is problematic since it introduces delay and complexity and limits scalability. Furthermore, in a network environment without strict quality of service guarantees where the instantaneous load isn't predictable it is hard to identify where the transcoding gateways should be placed. Another approach is to encode the media using a hierarchical representation that can be progressively decoded and assign the layers of the encoded signal to a set of distinct multicast addresses [, 3, 4, 5]. In this layered multicast transmission architecture each receiver individually chooses a quality suitable for the network and computing resources available, by joining an appropriate number of IP multicast groups. This is the target application for the video compression algorithm presented in this paper. While scalable encoding schemes based on the standard video compression algorithms have been designed (MPEG- scalable profile [], H.63+ []), the scalability requirement has clearly been added as an afterthought, resulting in high complexity and suboptimal performance. The goal of the work presented here is to design a compression algorithm with the scalability property as one of the fundamental requirements. II. LAYERED VIDEO COMPRESSION ALGORITHMS A layered video encoding is a representation that splits a digital video signal into a number of cumulative layers such that a progressively higher quality signal can be reconstructed the more layers are used in the decoding process. The layering can be performed in three ways, viz. spatial layering, temporal layering and layered quantization (also known as signal-to-noise-ratio scalability). In spatial layering the video can be reconstructed at successively higher spatial resolutions, while temporal layering implies that the frame rate of the video sequence can be progressively increased. In signal-to-noise-ratio (SNR) layering the quantization of the video images is refined with each added layer. While all three techniques result in a layered bitstream the nature of the layering is very different and address different aspects of the heterogeneity problem. With spatial layering the resolution of the decoded video images can be chosen depending on the resolution of the display. Temporal layering provides different levels of frame update dynamics in the video whereas SNR scalability varies the compression distortion of each individual frame. The type of layering that is most suitable depends on the application, on user-preference and on the level of overall scalability desired. A good scalable video codec should ideally provide all three types of layering simultaneously so that each decoder can individually trade-off between spatial resolution, temporal resolution and fidelity, given a certain resource limit. Thus the three layering techniques should be orthogonal so that they can be applied independently. The key challenge when designing a layered video compression

2 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications algorithm is to keep the compression efficiency high while providing a high level of scalability. Intuitively, a non-scalable codec should perform more efficiently compared to a scalable at a given bandwidth or distortion level. This assumption was verified by Equitz and Cover who proved that a progressive encoding can only be optimal if the source possesses certain Markovian properties []. Nevertheless, a number of layered video codecs have been proposed. The scalable mode of H.63+ defines a layered codec that provides all three modes of layering discussed above. In H.63, as well as in the MPEG video coding standards, spatial redundancies within individual images are reduced by the discrete cosine transform applied to eight-by-eight pixel blocks. Predictive coding with motion compensation is performed in the pixel domain with reference to a past frame, or bi-directionally with reference to both past and future frames. Spatial scalability is achieved by subsampling each frame until the desired resolution of the base layer is reached. The lowresolution image thus achieved is compressed using predictive coding and DCT whereupon the frame is decompressed and upsampled so that an error-signal constituting the enhancement layer can be computed. The process is repeated for each enhancement layer. SNR scalability is achieved in basically the same manner, except that instead of resampling the frames the binsize of the quantizer is refined at each level. Temporal scalability is achieved by assigning bi-directionally predicted pictures to the refinement layers to increase the frame rate of the decoded video. The scalable modes of MPEG and H.63+ are working in basically the same way. The basic problem is that the prohibitively high complexity introduced limits the total number of layers that are feasible. Also, the efficiency of the coding can be expected to be far worse compared to the baseline algorithm, although very little experimental results have been published. Another class of scalable video codecs are based on the discrete Wavelet transform (DWT). Since the DWT, when used to reduce spatial redundancies in image compression, is applied to the whole image as opposed to the block-based DCT, the algorithm provides a multiresolution image representation without the need for an explicit subsampling operation. The Wavelet coefficients can be progressively quantized in the same way as is performed in the block-based algorithm. Alternatively, Shapiro's Embedded Zerotree Wavelet (EZW) coding [4] or Said's and Pearlman's related algorithm based on set partitioning in hierarchical trees (SPIHT) [5] could be used to successively refine the coefficients. The key issue in Wavelet-based video codec design is how to exploit the temporal correlation between adjacent images to increase compression performance. One approach, pioneered by Taubman and Zakhor, is to extend the D DWT to three dimensions and apply the transform in the temporal dimension as well [,, 3, 7, 9]. Apart from a dramatic increase in computational complexity this approach also has the drawback of requiring images to be buffered prior to transmission, for at least as many frames as there are iterations of the wavelet transform. This generates an unacceptable coding delay for delay-sensitive applications. Another approach is to perform predictive coding and motion compensation in the pixel domain and then to compress the residual images using the DWT. This scheme is inherently incompatible with the scalable layering mode however, since a full resolution frame needs to be decoded before motion compensation can be performed. Another problem is that block-based motion compensation often results in blocking artifacts at high quantization ratios. This can be avoided by using overlapping block motion compensation [3, 8]. Yet another approach is to perform predictive coding and motion compensation in the transformed domain. The main obstacle with this type of coding lies in the fact that the Wavelet transform is translationally variant causing motion compensation to perform poorly [5, 6]. A remedy for this is to apply an antialiasing filter to the wavelet coefficients prior to motion estimation [7]. Needless to say, this increases the already high complexity associated with motion compensation. The algorithm presented in this paper is targeted at multipoint videoconferencing in heterogeneous environments. With this application in mind the following assumptions have guided the design:. Coding delay is of paramount importance. The coder is therefore not allowed to buffer frames in order to process two or more frames as a unit prior to transmission. Consequently, only temporal prediction with respect to previous frames is permissible, not bi-directional prediction. Three-dimensional subband transform coding is not viable either.. The video content is assumed to be reasonably static, without camera pans and limited scene motion. With this assumption the high complexity of motion compensation cannot be motivated and is therefore omitted. 3. The encoding should support a hybrid of spatial, temporal and SNR scalability enabling each receiver to trade-off between resolution, frame rate and distortion. 4. The algorithm should be reasonably lightweight so that software-only implementation on general-purpose processors is feasible. III. A NEW WAVELET-BASED VIDEO CODING ALGORITHM WITH LOW DELAY In an attempt to leverage off the excellent scalability and compression performance for still images provided by Shapiro's embedded zerotree wavelet coding (EZW), an extension to the EZW algorithm to also exploit temporal correlations between wavelet coefficients of previously processed frames has been developed. We call this novel algorithm EZWTP, for embedded zerotree wavelet coding with temporal prediction. In order to explain the algorithm let's first recapitulate Shapiro's classical EZW algorithm for still image compression. A. Embedded Zerotree Wavelet Coding for Still Images The first step of EZW coding is to decompose the input image into an octave-band pyramid, using a D DWT. A two-level decomposition of an image is shown in Fig.. The EZW algorithm produces an embedded bitstream (in the sense that it can be truncated at any point to achieve any desired bitrate) by ordering the coefficients of the subbands so that the most significant coefficients are coded first. Then the correlation between corresponding coefficients in subbands of the same orientation is exploited by introducing the concept of the zerotree data structure. The zerotree data structure, illustrated in Fig., is an association between coefficients in subbands of the same spatial orientation in a tree-like structure. Each coefficient, with the exception of the coefficients of the lowest frequency subband and the three highest-frequency subbands, is considered to be the parent of four coefficients (the children) at the next finer scale with the same spatial orientation. For a given parent, the set of child coefficients, the children's children and so on are called zerotree descendants. The highest frequency coefficients have no children and are thus never parents. The coefficients of the lowest frequency subband each have three child coefficients at the corresponding spatial positions in the horizontal, vertical and diagonal refinement subband of the same level, as indicated in Fig.. The algorithm progresses iteratively alternating between two passes, the dominant pass and the subordinate pass. In

3 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications 3 the dominant pass the coefficient values are identified as significant or insignificant with respect to a threshold value T i that is decreased (typically halved) for each iteration. The coefficients c x,y that are found to be significant at quantization level i, that is c x,y > T i are encoded with one of two symbols (POS or NEG) depending on the sign of the coefficient. A coefficient that has been found to be significant is set to zero to prevent it from being encoded as POS or NEG in subsequent dominant passes. Then the magnitude of the coefficient is inserted into a list called the subordinate list, used in the subordinate pass. The insignificant coefficients are considered to be zero at the current quantization level and are coded as either zerotree roots (ZTR) or isolated zeros (IZ). A coefficient is coded as a ZTR if all its descendants in the zerotree rooted at the coefficient are insignificant with respect to the current threshold. Otherwise, if any of its descendants are significant, the symbol is coded as an IZ. A coefficient that is the descendant of a previously coded zerotree root is set to zero and not encoded in this pass. The dominant pass processes all coefficients in a well-defined scanning order, subbandby-subband, from low-frequency to high-frequency subbands, encoding the coefficients that are not zerotree descendants with a codeword from a four-symbol alphabet. Since the highest frequency subbands don't have any zerotree roots, a ternary alphabet is used to encode those coefficients. After the dominant pass is completed the subordinate pass processes each entry of the subordinate list refining the coefficient value to an additional bit of precision. This is done by using a binary alphabet to indicate whether the magnitude of a coefficient is higher or lower than half the current threshold value. In effect, this corresponds to a quantizer binsize being halved for each subordinate pass. The algorithm alternates between the dominant and the subordinate pass, halving the threshold value for each iteration, until the desired precision is achieved or a bandwidth limit is met. The symbols that are output from the dominant pass (POS, NEG, ZTR and IZ) are entropy-coded using arithmetic coding. An adaptive arithmetic coder can be used to dynamically update the probabilistic model throughout the encoding process. In practice this is done by maintaining a histogram of symbol frequencies as described in [6]. To further improve the entropy coding, a number of histograms can be used and the selection of probabilistic model for each encoded symbol is conditioned on whether the coefficient's parent and left neighbor coefficient are significant or not. This results in four histograms for the dominant pass. In the subordinate pass a single histogram is used. Fig. Two-level dyadic Wavelet decomposition of an image LL LH LH HL HH HL HH Fig. Parent-child relationship of subbands. A zerotree rooted at the LL subband is also shown. B. The EZWTP Algorithm To extend the EZW-algorithm to video coding without introducing substantial coding delays and prohibitively high complexity, a temporal prediction scheme without motion compensation is devised. The temporal prediction uses only the previously coded frame as reference. For robustness to packet loss, intra-coding is employed at regular intervals so that the decoder can be resynchronized. Thus, two types of encoded images are present in the output video stream: intra-coded frames and predictive frames (I-frames and P-frames). The I-frames are coded using the traditional EZW-algorithm. For the P-frames, two new symbols are introduced in the dominant pass: zerotree root with temporal prediction (ZTRTP) and isolated zero with temporal prediction (IZTP). A coefficient is coded as ZTRTP in the dominant pass if it cannot be coded as a ZTR, but the difference between the coefficient and the coefficient at the same spatial location in the previous frame is insignificant with respect to the current threshold and so is the difference between each descendant of the coefficient and the corresponding descendant in the previous frame. Thus, a temporally predicted zerotree is an extension of the zerotree data structure to include coefficients of the same subbands in the previous frame. This relationship is illustrated in Fig. 3. A coefficient that is insignificant, but is not a ZTR or ZTRTP (or a descendant), is coded as an isolated zero. A significant coefficient that is not a ZTRTP is coded as an IZTP if the difference between the coefficient and the corresponding coefficient in the previous frame at the current quantization level is insignificant. Note that when computing the difference between a coefficient's value and the value of the coefficient at the same spatial location in the previous frame, we must use the approximation of the coefficient value corresponding to the precision of the current pass of the algorithm. This is because in order to decode a coefficient's value at a precision corresponding to the i:th refinement level, the decoder should only be required to decode the previous frame's coefficients at refinement levels,,..i. Otherwise the SNR scalability criterion would be violated. Consequently, the coder and decoder must keep the coefficient values of each refinement level of a frame for reference when coding or decoding the next predictive frame. Although this results in a substantial memory requirement, it doesn't introduce any buffering delay, since a frame is still transmitted once it is coded. Coefficients that are not coded as ZTRTP, ZTR, IZTP or IZ are coded as POS or NEG depending on the sign as in the original EZW algorithm. Note however that when a coefficient is found to be significant and pushed onto the subordinate list, after previously having been coded with temporal prediction (ZTRTP, temporally predicted zerotree descendant or IZTP), it is the magnitude of the difference between the coefficient and the coefficient used for the temporal prediction that should be recorded. It must also be

4 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications 4 remembered that this magnitude value represents a differentially coded coefficient. In this way the same threshold value can be used for refinement of both coefficient magnitudes and coefficient difference magnitudes. Since the state of the algorithm implicitly encodes this information no extra signaling is needed between the coder and decoder. Note also that the successive approximations of coefficient values that the decoder will reconstruct can be generated as intermediate results of the encoding process without extra cost. intended application of multipoint video communication systems must use either a static model or introduce synchronization points in the media (e.g. for every I-frame), where the probability models are propagated from the coder to the decoder. The advantage of using an adaptive arithmetic coder is not so significant that it motivates the added complexity of maintaining symbol frequency histograms, so a static model is generally preferred. LL LH HL HH HL LL LH HL HH HL input image DWT I- or P- frame? EZWTP P-frame I- frame EZW frame memory arithmetic coding output LH HH LH HH frame i- frame i Fig. 4 Schematic diagram of the EZWTP encoding process Fig. 3 Spatial and temporal relationships of the coefficients belonging to a temporally predicted zerotree rooted at subband LL of frame i The subordinate pass works in the same way as in the original EZW algorithm apart from the fact that some of the magnitude values on the subordinate list now represent the prediction error term of a coefficient relative to the corresponding coefficient in the previously decoded frame. The state of the decoder when the coefficient is added to the subordinate list determines whether it is a prediction error term or a coefficient magnitude value and this information is kept in the subordinate list. The arithmetic coding of the symbols is performed using codewords from five different alphabets. For I-frames the three alphabets of the original EZW algorithm are used, viz. a four-symbol alphabet for all subbands except the highest frequency subbands of the dominant pass, a ternary alphabet for the highest frequency subbands and a binary alphabet for the subordinate pass. For P- frames a six-symbol alphabet is used for all subbands except the highest frequency ones (ZTR, IZ, ZTRTP, IZTP, POS, NEG), a foursymbol alphabet is used for the highest frequency subbands, where ZTRTP and ZTR cannot occur, and a binary alphabet for the subordinate pass. The conditioning of the statistical model used by the arithmetic coder in the dominant pass is performed with respect to whether the parent and left neighbor of a coefficient is significant, as in the original EZW algorithm, but also with respect to whether the corresponding coefficient in the previous frame is significant. This increases the performance of the arithmetic coder. Another difference compared to the original EZW algorithm is that with the addition of temporal information, there is now a way to condition the statistical model to be used for arithmetic coding of the symbols resulting from the subordinate pass. Since the coefficients at the same spatial location in adjacent frames exhibit a strong correlation, the probability is higher that the coefficient will be refined in the same direction as the corresponding coefficient in the previous frame. Thus the arithmetic coding of the symbols from the subordinate pass can be enhanced by temporal conditioning. The arithmetic coding can be based on either static, predefined, probability models or adaptive models based on histograms of symbol frequencies. However, since the decoder should be able to partially decode the encoded bitstream a fully adaptive arithmetic coding, where symbol probabilities are updated for every coded symbol, cannot be used. Thus, in order not to violate the scalability criteria and to be resilient to packet loss, the C. EZWTP Codec Design The EZWTP encoder consists of the following four components:. colorspace conversion and component subsampling,. transform coding, 3. zerotree coding with built-in temporal prediction, 4. arithmetic coding. The colorspace conversion transforms the input color video signal into a luminance signal (Y) and two color-difference chrominance signals (Cr and Cb). Since the human visual system is more sensitive to variations in luminosity than in hue, the chrominance signals are subsampled by two horizontally and vertically. The colorspace conversion and subsampling operations decorrelates the components and reduces the bandwidth to half of the original signal. The encoding is then performed separately on each of the three components. The encoding process is illustrated schematically in Fig. 4. ) Transform Coding and Spatial Scalability The wavelet transform decomposes the input images into subbands representing the frequency content of the image at different scales and orientations. The transform is implemented by applying a pair of band-splitting filters to the image. The filtering process is repeated on the lowest-frequency subband a finite number of steps, resulting in a pyramid of wavelet coefficients like the one depicted in Fig.. For the implementation of the EZWTP codec presented in this paper the filters designed by Antonini et al. [8] were chosen, since they have been found to give good performance for image coding [9]. The transform is iterated on the low-pass subband until the size is considered small enough, e.g. for CIF-size images (35x88), five iterations are done for the luminance component and four for the chrominance. Thus, for CIF video five spatial resolution levels are obtained each of which (except the LL-band) contains three refinement signals for horizontal, vertical and diagonal detail. The spatial layering can be performed on subband level resulting in 3x5+ = 6 spatial layers, for CIF images. Such a fine granularity for spatial scalability is probably unnecessary for most applications, suggesting that the subbands should be coalesced into fewer layers.

5 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications 5 inter-frame coding without motion compensation perform reasonably well I-frame P-frame IP-frame image size (bpp) Fig. 5 Inter-frame dependencies for intra-coded frames (I-frames), predicted frames (P-frames) and intra-predicted frames (IP-frames). ) Temporal Scalability The temporal scalability requirement restricts the inter-frame dependencies that the predictive coding is allowed to establish. Since a P-coded frame cannot be decoded unless the I- or P-frame it is predicted from has been decoded, such inter-frame dependencies must be confined to the same layer or to temporally antecedent layers. P-frames are generally predicted from the immediately preceding frame, since the temporal correlation usually diminishes rather quickly. One approach is to employ a two-layer model wherein all I-frames are assigned to the base layer and all P-frames to a single refinement layer. To increase the number of temporal layers possible some (or all) P-frames can be predicted from the previous I-frame instead of from the immediately preceding frame. Fig. 5 illustrates a temporal layering arrangement with three temporal layers, where the P-frames temporally equidistant from two I-frames are coded with reference to the previous I-frame and the intermediate P-frames are coded relative to the immediately preceding P- or I-frame. IV. PERFORMANCE In this section a number of performance measurements are presented that evaluates the efficiency of the codec in terms of scalability, compression rate and reconstructed image quality. The compression efficiency for a given bandwidth limit is compared to that of a non-scalable codec in order to quantify the sacrifice in compression rate that the layering requirement imposes. A. Inter-frame Compression Performance In order to investigate how much compression efficiency is gained by the predictive coding introduced in the EZWTP algorithm a number of measurements were performed comparing the compression rate obtained for different ratios between I-frames and P-frames. In Fig. 6 the compressed image size in bits-per-pixel is plotted for each of the first images of the CIF akiyo video sequence for eight P-frames per I-frame. Fig. 7 shows the same plot for I-frames only. The former I-frame/P-frame layout thus supports two temporal layers, whereas the latter supports any number of temporal layers (since there are no inter-frame dependencies). Each line in Fig. 6 and Fig. 7 represents a quantization level, resulting from the SNR scalable EZWTP coding. The compression performance for P-frames can be seen to be about twice the performance for I-frames, for each quantization level. Since the akiyo test sequence contains a typical "head and shoulders" scene it can be assumed to be fairly representative for the kind of video content the algorithm is targeted for. The low-motion nature of the video makes. image Fig. 6 Size of each compressed image (in bits-per-pixel) for the CIF akiyo test sequence at 8 P-frames per I-frame image size (bpp) image Fig. 7 Size of each compressed image (in bits-per-pixel) for the CIF akiyo test sequence, with I-frames only In Fig. 8 the mean size of a compressed image of the akiyo sequence is plotted against the proportion of P-frames per I-frame. Again, each curve represents a quantization level. It can be seen that the inclusion of inter-frame coding is highly beneficial to the overall compression efficiency, and that a coding-strategy with one I-frame every fourth to sixth frame can be adopted without affecting compression performance significantly. average image size (bpp) P-frames per I-frame Fig. 8 Mean compressed image size (in bits per pixel) depending on the number of P-frames for each I-frame B. Overall Compression Efficiency To analyze the penalty on compression efficiency for a given

6 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications 6 bandwidth that the scalability requirements impose and to compare the performance of the EZWTP algorithm to a popular, widely used codec, EZWTP was compared with a non-scalable MPEG- codec []. In order for the comparison to be as fair as possible, and to reflect the target application for the EZWTP codec, the MPEG codec was configured to use I- and P-frames only (no B-frames) and to have the same I-frame/P-frame ratio. The overall compression performance was quantified by computing the peak-signal-to-noiseratio (PSNR) for a number of bandwidth levels (measured in bits-perpixel). The PSNR was computed on the luminance component only, whereas the bandwidth refers to all three components. Due to the SNR scalability of the EZWTP codec all distortion levels could be decoded from a single encoded source, whereas for the non-scalable MPEG codec the encoding was done multiple times with different target bandwidths. In Fig. 9 the compression performance of the first frames of the CIF akiyo sequence is shown. It is clear that the MPEG codec outperforms the EZWTP codec with as much as 3 db. PSNR bpp EZWTP MPEG- inter-frame compression of EZWTP performs very well on the lowmotion akiyo sequence (cf. Fig. 6), the motion-compensated interframe coding of MPEG performs even better. The susie sequence contains slightly more motion and since the performance of MPEG to a higher degree depends on the efficacy of the inter-frame compression compared to EZWTP, the reduced P-coding efficiency has a larger impact. C. Scalability The scalability of the encoding in terms of bandwidth and quality of the reconstructed signal is illustrated in Fig. and Fig., calculated over the first frames of the CIF akiyo sequence. Only the effects of SNR- and spatial layering are considered. A five-level wavelet transform was used for the encoding, resulting in a total of 6 subbands (three refinement subbands per level plus the base layer). Each subband was assigned a unique spatial layer for the purpose of these measurements. The quantization of the wavelet coefficients was divided into refinement layers. Thus, a hierarchical structure of *6=9 layers was created. For most applications such a fine granularity is probably not needed, indicating that some layers should be merged. Fig. shows the cumulative bandwidth in kilobits per second (kbps) as a function of the number of spatial layers and quantization layers. The bandwidth was computed at a frame rate of 5 frames per second. As can be seen, increasing the sample precision (adding quantization layers) has a bigger effect on bandwidth consumption compared to an increase in spatial resolution. bandwidth (kbps) 6 Fig. 9 Compression efficiency of EZWTP compared to MPEG- for the CIF akiyo test sequence Fig. shows the compression efficiency of EZWTP and MPEG- for the first frames of the 4CIF susie test sequence. For this video source the EZWTP algorithm performs almost as well as the MPEG codec at low bitrates and even outperforms the MPEG codec at high bitrates quantization layers spatial layers PSNR bpp EZWTP MPEG- Fig. Bandwidth scalability of the encoding of the CIF akiyo sequence PSNR quantization layers spatial layers Fig. Compression efficiency of EZWTP compared to MPEG- for the 4CIF susie test sequence The reason why the EZWTP algorithm performs better relative to MPEG for susie compared to akiyo is probably related to the fact that the higher spatial resolution in the former case makes the superiority of the Wavelet transform over the DCT for spatial decorrelation be of higher significance. A contributing factor might be that although the Fig. Scalability of the encoding of the CIF akiyo sequence in terms of PSNR of decoded images. The corresponding reconstructed image quality for each quantization and resolution level is shown in Fig.. Here, image quality is quantified using the peak-signal-to-noise ratio of the original and reconstructed images. When computing the PSNR for frames decoded at a lower spatial resolution than the original, the

7 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications 7 reconstructed image was upsampled to the original dimensions prior to computing the mean square error to the original image. It is important to note that this is a statistical measure of correlation between signals that doesn't take psychovisual effects into considerations and hence is a poor estimator for perceptual quality. For instance there seems to be some anomaly resulting in a lowered quality when 4 subbands are used in the decoder, compared to using only 3. Upon visual inspection of the images, however, the higher resolution versions are subjectively preferable, although mathematically more distorted. The PSNR can be seen to depend approximately linearly on both quantization and resolution. The conclusions that can be drawn from these measurements are that finer quantization has a more profound effect on bandwidth consumption compared to increased spatial resolution, and that the reconstructed image quality (determined by the PSNR metric) depends, in some sense, equally on both parameters. Thus, when trading off between resolution and quantization distortion, the former should possibly be prioritized. However, in real applications, other factors like the video content, the type of application and user preference are likely to be of significant importance for this decision. Fig. 3 displays one frame from the akiyo test sequence decoded at three different resolutions and five different distortion levels. For image a (CIF resolution) 6 spatial layers and SNR layers were used in the decoding process. For b and c (QCIF resolution) spatial layers were used with 7 and 6 SNR layers respectively. For d and e (/6 CIF resolution) 8 spatial layers were used while the number of SNR layers were 7 and 5 respectively. In Table the PSNR of each image in Fig. 3 is listed together with the frame rate that can be supported, given the bandwidth limit of a particular network access technology. The spatial resolutions are also included. These measurements indicate what performance can be expected from the video coding in some relevant situations. TABLE EXAMPLES OF IMAGE QUALITY, SPATIAL RESOLUTION AND FRAME RATE AT BANDWIDTHS CORRESPONDING TO DIFFERENT NETWORK ACCESS TECHNOLOGIES image resolution PSNR fps target access technology a CIF 4. 5 T (.5 Mbps) b QCIF xISDN (56 kbps) c QCIF 8.6 xisdn (8 kbps) d /6 CIF xisdn (8 kbps) e /6 CIF 4.5 modem (33 kbps) V. PROCESSING REQUIREMENTS One of the primary design goals of the EZWTP codec is that the computational complexity should be low enough for the algorithm to be possible to implement in software on general-purpose processors. Furthermore, the processing requirements should be scalable so that the coding and/or decoding complexity can be adjustable to the amount of CPU resources available for different types of end equipment. To analyze the complexity of the EZWTP algorithm we first note that the two major contributions to the overall complexity are the transform and quantization for the encoder and the inverse transform and the dequantization for the decoder. It is easy to see that the coding and decoding requirements are symmetric, since the inverse transform and the dequantization are simply the reverse processes of the forward transform and the quantization. Therefore we present the complexity analysis for the decoder only, since the scalability property of the algorithm is most highlighted in the situation where one encoded stream is decoded at many different quality and complexity levels for a collection of heterogeneous decoders. The computational complexity is estimated depending on the number of levels of the inverse transform and the number of iterations of the zerotree decoding that is performed. In this way we can analyze the scalability of the processing requirement in relation to the spatial resolution and compression distortion of the reconstructed video. One iteration of the wavelet transform is implemented by applying a low-pass and a high-pass filter to the pixel values of each image. For the next iteration the transform is applied to the low-frequency subband which has a resolution of a quarter of the original. Thus, for L levels of the transform the processing requirement is proportional to the number of multiplications performed, which is b a c d Fig. 3 A frame of the akiyo test sequence decoded at three different resolutions and five different distortion levels e L i= M 4 fn i, ()

8 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications 8 where f is the filter tap length, n is the number of pixels of the full image, M is the total number of transform levels executed by the encoder, and L M is the number of levels of the inverse transform effected by the decoder. The zerotree decoding with temporal prediction is performed in two passes; the dominant pass and the subordinate pass. In the dominant pass the wavelet coefficients that were found to be significant in the corresponding dominant pass of the encoder are input and decoded. The ZTR, ZTRTP, IZ and IZTP symbols are also read and the coefficients affected are set to zero or to the value predicted from the previous image. The significant coefficients are added to the subordinate list for further processing. In the dominant pass each coefficient is updated once, so the processing power for each pass is proportional to the number of coefficients, resulting in a total number of Pn M L 4 coefficients to be processed, where P is the number of iterations of the EZWTP decoding algorithm (i.e. the number of quantization levels decoded). In the subordinate pass each coefficient in the subordinate list is processed and refined to an extra bit of precision as determined by the symbols read from the input stream. The processing power for each iteration of the subordinate pass is hence proportional to the number of significant coefficients for that level. If no temporal prediction is performed (i.e. I-coding) the number of significant coefficients can be found empirically to be approximately doubled for each pass. With temporal prediction the number of significant coefficients is reduced, but we nevertheless assume a doubling quantity also for P-frames, appreciating that the complexity estimation will be somewhat pessimistic. This gives us a complexity for the subordinate pass that is proportional to P j= 4 M L n Q j where Q is the total number of quantization levels computed by the encoder, and thus, P Q. A linear combination of (), () and (3) gives the total complexity, C EZWTP (L, P). That is, for some positive proportionality constants C, C, C 3, where the filter length in () has been included in the C constant, C EZWTP (L, P) = L P n Pn C + C + M i C M L n M L Q j i= j= n L+ L L Q P+ C (4 ) 4 PC C ( M ( ) ) As can bee seen in (4), the complexity of the EZWTP decoding grows exponentially with respect to the number of transform levels (L). This is not surprising since the number of pixels to process increases by a factor four when the width and height of the images are doubled. With respect to the number of quantization levels (P), the EZWTP complexity increases by a linear term and an exponential 4 = () (3) (4) term, accounting for the dominant and subordinate passes respectively. To verify the theoretically deduced complexity estimation in (4) the execution time of the EZWTP implementation when decoding the akiyo test video sequence was measured for different values of L and P. The proportionality constants were empirically determined from decoding time measurements. In Fig. 4 decoding time is plotted against the number of quantization levels while keeping the number of transform levels constant. As can be seen, the decoding time corresponds reasonably well with the theoretically estimated curve. In Fig. 5 the decoding time is plotted against the number of transform levels, while keeping the number of quantization levels constant. Again, a reasonable correspondence is found indicating that the complexity estimation in (4) is sound. decoding time (ms) SNR levels Fig. 4 Decoding time as a function of the number of quantization levels decoding time (ms) IDWT levels Fig. 5 Decoding time as a function of the number of inverse transform levels From looking at the graphs in Fig. 4 and Fig. 5 it appears as if the number of IDWT levels chosen has a larger impact on decoding time than the number of quantization levels. Thus, when trading off between resolution and quantization distortion in the decoder, from a complexity standpoint a refinement of the quantization precision might be preferable compared to an increased resolution. The shape of the graph in Fig. 4 suggests that the linear term of P in (4) is dominant over the exponential, for SNR levels below, resulting in an approximately constant increase in complexity, compared to the apparent exponential increase imposed by a higher resolution level. To verify this observation we differentiate the complexity function C EZWTP (L, P) with respect to L and P and form the quotient of the derivatives. The ratio thus obtained represents the relative impact on computational complexity of refining the spatial resolution versus the quantization precision.

9 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications 9 C L C P ln 4 4C = C C + PC + + ln C 3 3 Q P+ ( ) P Q+ P Q+ 8C + PC + C3 C P Q+ + C3 ln C iff 8C PC ln (5) + (6) Since P > / ln 4 C /C, the relation in (6) is trivially true and thus the ratio in (5) is always greater than one, implying that the increase in computational complexity is always affected more by an increase in resolution compared to an increase in quantization precision, irrespective of L, P and the proportionality constants. This suggests that for computationally constrained devices, refined quantization might be preferred over increased resolution. Note that in this analysis we have calculated the change in computational cost associated with a change in resolution corresponding to three additional spatial subbands being used in the decoding process. That is, we don t consider the effect on complexity of adding the spatial subbands of a transform level independently. Since the improvement in reconstructed image quality is most profound when adding a spatial subband of the next resolution level (cf. Fig. ), the conclusions are still consistent. VI. SUMMARY AND CONCLUSIONS Real-time multipoint Internet videoconferencing applications require highly scalable video encoding and compression algorithms with minimal coding delays. This paper has presented a video compression algorithm that produces a layered bitstream that can be decoded at different quality levels depending on the amount of resources available to the decoder in terms of network bandwidth, computational capacity and visualization capabilities. The algorithm, called EZWTP, has been designed with the scalability and real-time properties as primary requirements, while trying to maintain high compression efficiency and low computational complexity. Computational complexity is kept low by excluding motion compensation. The motivation for doing so is that the target application (Internet videoconferencing) implies that a reasonably low-motion video content can be assumed. The inter-frame compression of EZWTP was shown to give a substantial compression performance for low-motion video scenes. In comparison to a popular non-layered codec (MPEG-), the EZWTP codec was shown to exhibit competitive compression performance for high-resolution video, due to the superior spatial decorrelation properties of the wavelet transform compared to the discrete cosine transform. For lower resolution video, non-scalable codecs with motion compensation typically outperform the EZWTP algorithm. The decoder can trade off between frame rate, spatial resolution and compression distortion based on local constraints and user preference. Complexity and performance analyses showed that for computationally constrained devices, enhanced quantization might be favored over an increased spatial resolution, while the opposite discrimination could be advocated for bandwidth constrained instances of the decoder. The temporal layering has a linear impact on both decoding time and bandwidth consumption. Although the computational power of processors and the capacity of network infrastructure will continue to increase, heterogeneity will persist. It can thus be argued that scalability in terms of performance and resource consumption should be considered a more important feature of a video-coding algorithm, than sheer compression efficiency, when targeting applications like Internet videoconferencing. This sentiment has inspired the work presented in this paper. REFERENCES [] D. Taubman and A. Zakhor, "Multirate 3-D subband coding of video," IEEE Trans. Image Processing, vol. 3, no. 5, pp , Sep [] C. I. Podilchuck, N. S. Jayant and N. Farvardin, "Three-dimensional subband coding of video," IEEE Trans. Image Processing, vol., no., pp. 5-39, Feb [3] J. R. Ohm, "Three-dimensional subband coding with motion compensation," IEEE Trans. Image Processing, vol. 3, no. 5, pp , Sep. 99. [4] J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," IEEE Trans. Image Processing, vol. 4, no., pp , Dec [5] A. Said and W. Pearlman, "A new, fast and efficient image codec based on set partitioning in hierarchical trees," IEEE Trans. Circuits and Syst. for Video Technol., vol. 6, no. 3, pp. 43-5, June 996. [6] I. H. Witten, R. Neal and J. G. Cleary, "Arithmetic coding for data compression," Communications of the ACM, vol. 3, pp. 5-54, June 987. [7] X. Yang, K. Ramchandran, "Scalable wavelet video coding using aliasreduced hierarchical motion compensation," IEEE Trans. Image Processing, vol. 9, no. 5, May. [8] M. Antonini, M. Barlaud, P. Mathieu and I. Daubechies, "Image coding using wavelet transform," IEEE Trans. Image Processing, vol., no., April 99. [9] D. Villasenor et al., "Wavelet filter evaluation for image compression," IEEE Trans. Image Processing, Aug [] W. Equitz and T. Cover, Successive refinement of information, IEEE Trans. Information Theory, vol. 37, pp , Mar. 99. [] G. Cote, B. Erol and F. Kossentini, H.63+: Video coding at low bit rates," IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 7, pp , Nov [] MPEG-, ISO/IEC 388. Generic coding of moving pictures and associated audio information, Nov [3] M. T. Orchard, G. J. Sullivan, Overlapped block motion compensation: an estimation-theoretic approach, IEEE Trans. Image Processing, vol. 3, no. 5, pp , Sept [4] M. Vetterli and J. Kovacevic, Wavelets and Subband Coding. Englewood Cliffs, NJ: Prentice-Hall, 995. [5] K. Tsunashima, J. B. Stampleman, and V. M. Bove, A scalable motioncompensated subband image coder, IEEE Trans. Commun., vol. 4, pp.894 9, Apr [6] A. Nosratinia and M. Orchard, Multi-resolution backward video coding, in IEEE Int. Conf. Image Processing, vol., Washington DC, Oct. 995, pp [7] K. Metin Uz and M. Vetterli, Interpolative multiresolution coding of advanced television with compatible subchannels, IEEE Trans. Circuits Syst. Video Technol., vol., no., pp , Mar. 99. [8] M. Ohta and S. Nogaki, ``Hybrid picture coding with wavelet transform and overlapped motion-compensated interframe prediction coding,'' IEEE transactions on Signal Processing, vol. 4, no., pp , Dec [9] Y. Chen and W. Pearlman, Three-dimensional subband coding of video using the zero-tree method, in Proceedings of SPIE - Visual Communications and Image Processing, Orlando, Mar. 996, pp [] S. McCanne, M. Vetterli, and V. Jacobson, "Low-complexity video coding for receiver-driven layered multicast", IEEE Journal on Selected Areas in Communications, vol. 6, no. 6, pp. 983-, Aug [] A. C. Hung, "PVRG-MPEG CODEC.", Portable Video Research Group (PRVG), Stanford University, June 4, 993.

10 M. Johanson, A Scalable Video Compression Algorithm for Real-time Internet Applications [] N. Shacham, Multicast routing of hierachical data, in Proceedings of the International Conference on Computer Communications, Chicago, June 99, pp. 7-. [3] T. Turletti and J. C. Bolot, Issues with multicast video distribution in heterogeneous packet networks, in Proceedings of the Sixth International Workshop on Packet Video, Portland, Sept [4] S. McCanne, V. Jacobson, and M. Vetterli, Receiver-driven layered multicast, in Proceedings of SIGCOMM 96, Stanford, Aug [5] S. McCanne, Scalable video coding and transmission for Internet multicast video, Ph.D. thesis, University of California, Berkeley, Dec Mathias Johanson was born October, 97 in Gothenburg, Sweden. He received his M.Sc. in Computer Science in 99 and is currently pursuing a Ph.D. degree at Chalmers University of Technology in Gothenburg, while being employed by Alkit Communications. His research interests are in the areas of visual communication systems, computer networking and signal processing.

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