In video coding systems using adaptive arithmetic coding to compress texture information, the employed symbol probability models need to be retrained every time the coding process moves into an area with different texture. To avoid this inefficiency, we propose to replace the probability models used in the original coder with multiple switchable sets of probability models. We determine the model set to use in each spatial region in an optimal manner, taking into account the additional signaling overhead. Experimental results show that this approach, when applied to H. 264/AVC's context-based adaptive binary arithmetic coder (CABAC), yields significant bit-rate savings, which are comparable to or higher than those obtained using alternative improvements to CABAC previously proposed in the literature

Barbarien, Joeri

Lambert, Peter

Van de Walle, Rik

Vermeirsch, Kenneth

English

Ghent University Academic Bibliography

  biblio.ugent.be  The UGent Institutional Repository is the electronic archiving and dissemination platform for all UGent research publications. Ghent University has implemented a mandate stipulating that all academic publications of UGent researchers should be deposited and archived in this repository. Except for items where current copyright restrictions apply, these papers are available in Open Access.  This item is the archived peer‐reviewed author‐version of: Title: Region‐Adaptive Probability Model Selection for the Arithmetic Coding of Video Texture Authors: K. Vermeirsch, J. Barbarien, P. Lambert, and R. Van de Walle  In: Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1537 ‐ 1540, May 2011. To refer to or to cite this work, please use the citation to the published version: K. Vermeirsch, J. Barbarien, P. Lambert, and R. Van de Walle (2011). Region‐Adaptive Probability Model Selection for the Arithmetic Coding of Video Texture. . International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1537‐1540. REGION-ADAPTIVE PROBABILITY MODEL SELECTION FOR THE ARITHMETICCODING OF VIDEO TEXTUREK. Vermeirsch, J. Barbarien, P. Lambert, and R. Van de WalleK. Vermeirsch, P. Lambert, and R. Van de Walle are with the Department ofElectronics and Information Systems, Faculty of Engineering, Ghent University, Belgium.J. Barbarien is with the Department ofElectronics and Informatics (ETRO), Vrije Universiteit Brussel, Belgium.ABSTRACTIn video coding systems using adaptive arithmetic coding tocompress texture information, the employed symbol probabil-ity models need to be retrained every time the coding processmoves into an area with different texture. To avoid this ineffi-ciency, we propose to replace the probability models used inthe original coder with multiple switchable sets of probabilitymodels. We determine the model set to use in each spatial re-gion in an optimal manner, taking into account the additionalsignaling overhead. Experimental results show that this ap-proach, when applied to H.264/AVC’s context-based adaptivebinary arithmetic coder (CABAC), yields significant bit-ratesavings, which are comparable to or higher than those ob-tained using alternative improvements to CABAC previouslyproposed in the literature.Index Terms— Video coding, CABAC, arithmetic cod-ing, adaptivity, context modeling1. INTRODUCTIONH.264/AVC [1], which represents the current state-of-the-art in video coding technology, is typically configured to useContext-based Adaptive Binary Arithmetic Coding (CABAC)[2] for the entropy coding of texture and motion informationin application scenarios demanding optimal compression per-formance.In the literature, improvements to CABAC have focusedon a number of areas. Several authors [3, 4, 5] have proposedto replace the simple CABAC data modeling by the more so-phisticated Context-Tree Weighting (CTW), reporting about1 to 3 % bit-rate savings for typical quality settings, at thecost of a significant increase in computational complexity, asreported in [5]. Belyaev et al. [6] use a virtual sliding win-The research activities described in this paper were funded by GhentUniversity, the Interdisciplinary Institute for Broadband Technology (IBBT),the Institute for the Promotion of Innovation by Science and Technology inFlanders (IWT), the Fund for Scientific Research–Flanders (FWO–Flanders),and the European Union.dow (VSW) for probability model estimation and report ratesavings between 0.3 and 0.8 % for realistic quality settings.In this paper, we improve the efficiency of CABAC en-tropy coding by introducing the concept of region-adaptiveprobability model selection. This improvement is not onlyapplicable to CABAC in H.264/AVC but can be adapted toany video or image coder, present or future, which employsadaptive probability models.2. REGION-ADAPTIVE CODING METHODTypical video scenes comprise a number of distinct regions,each having unique texture characteristics, resulting in differ-ent symbol statistics for the quantized transform coefficientsrepresenting the texture information, even if the transform ispreceded by a spatial or temporal prediction step. As theadaptive arithmetic coder visits the blocks of a video framein the typical raster scan order, the boundaries between thedifferent regions are often crossed multiple times. Each timesuch an event occurs, the coder’s probability models for tex-ture, which had been trained to the characteristics of the pre-vious region, need to be retrained to the characteristics of thenewly entered region. Before the model reconverges to thestatistics of the symbols in the newly entered region, manysymbols may have been coded suboptimally, resulting in apenalty in bit rate.As mentioned, this retraining of the probability modelsoccurs more often than necessary because of the raster blockscanning order universally used in video coders. One solu-tion is to remove this underlying cause, abandoning the rasterscan, instead coding each image region or object in turn.This poses several practical problems: it requires an imagesegmentation process to be performed prior to encoding, andit causes unpredictable non-sequential access to the framememory, making this solution hard to implement efficientlyin either software or hardware.The solution we propose, and which we call region-adaptive probability model selection, instead retains theraster scanning order of the coding process. The original...4321NEntropy codern Plps MPSbinsBinarizationbitstreamsyntax elementsupdatefetchFig. 1. Schematic overview of CABAC.set of probability models used to code texture informationis replaced by several switchable sets of probability models.Each frame is then partitioned into square tiles, and one ofthe available sets of probability models is associated witheach tile. Within a tile, only the active set of models is beingtrained and adapted; the other sets remain untouched, readyto be activated in other regions.3. SUMMARY DESCRIPTION OF CABACBefore giving a detailed description of our region-adaptiveprobability selection approach, a brief overview of CABACis given. For a full description, the reader is referred to [2]. Aschematic representation of the data flow in CABAC is pre-sented in Figure 1.Binarization: For reasons of computational complexityand uniformity, CABAC is based on an arithmetic coder,which only accepts binary input symbols. As a result, sym-bols which can take on more than two values need to bebinarized first, i.e., they are converted to two or more binarysubsymbols called bins prior to arithmetic coding. CABACemploys many different binarization schemes, including Ex-ponential Golomb and unary binarization. The type of bi-narization used depends on the source of the symbols beingcoded and has been determined based on the typical proba-bility distribution of the symbols generated by this source.Bin model selection and bin model update: Each bin is as-signed a binary probability model (bin model), and the state ofthis model is represented by a most probable symbol (MPS),which is a binary value, and the probability of the least prob-able symbol occurring. The model restricts to one of 64 dis-crete values, which implies that the state of a bin model canbe represented using only 7 bits.The assignment of a probability model to a bin is deter-mined by the source of the symbols, the associated binariza-tion scheme and, for some bins, by local context. For ex-ample, the model assignment for transform coefficients de-pends on the coding mode and on the position of the coef-ficient within the transform block. Consequently, there aremany hundreds of bin models employed in CABAC.The symbols composing the texture information in 4:2:0progressive video sequences include flags indicating the po-sitions of the nonzero transforms coefficients; and symbolsrepresenting the magnitudes of the nonzero coefficients; andtheir signs. The coding of these symbols involves 225 differ-ent bin models. This collection of probability models is, inthe context of this paper, collectively called a texture model.After a bin has been coded by the binary arithmetic coder,its associated bin model is updated. This is how the texturemodel adapts to the image region’s nonstationary character-istics. If the bin value matches the model’s MPS, then theMPS probability is increased by one step, ultimately converg-ing to nearly one. If, however, the bin value is not equal tothe MPS (but equal to the least probable symbol or LPS), theMPS probability is reduced toward 0.5 by multiple steps atonce. When the MPS and LPS become equally probable, theMPS and LPS may be interchanged.4. PROPOSED REGION-ADAPTIVE TEXTUREMODEL SELECTIONIn the proposed method, the image is subdivided into squaretiles of size S. Each tile may be coded using one ofN texturemodels. We now describe the tile coding procedure, followedby the determination of the S and N parameters.Tile coding procedure: At the start of a tile, the coderevaluates which is the best of the N texture models for cod-ing the tile’s syntax elements. For optimal compression effi-ciency, the N different texture models may be evaluated byeffectively coding all syntax elements using each of the avail-...4321NEntropy codern Plps MPSbinsBinarizationsyntax elementsfetch updatebitstreamRate minimizerFig. 2. Schematic overview of the proposed algorithm with exhaustive search.able texture models and comparing the resulting bit-rates, asillustrated in Figure 2. The texture model yielding the small-est bit-rate is retained and all other texture models are reset totheir previous states. Note that the time-consuming processesof binarization and bin model selection only need to be per-formed once, since their outcome does not depend on the cho-sen texture model. Furthermore, it should be observed that theeffective encoding operations for different texture models canproceed in parallel, allowing for a computationally efficientimplementation.As a low-complexity alternative to this exhaustive ap-proach, the coder can also determine the best texture modelwithout actually coding N times, by counting, for each tex-ture model, how many times a least-probable symbol wouldbe encoded. This metric provides a reliable indication of bit-optimality, except in those cases where two or more texturemodels would lead to comparable coding efficiency, in whichcase a misprediction is of little consequence anyway.Since only 7 bits are needed to represent a bin model, theadditional memory requirements for encoder and decoder arequite modest: each texture model has a memory footprint of197 bytes. When four texture models are used as in the ex-periments below, only 788 extra bytes are required.Tile size selection: To ensure decodeability, a certainamount of overhead bits need to be transmitted to indicatewhich texture model was used in each tile. This overhead issignaled inside the bit stream using CABAC and truncatedExponential Golomb binarization. The amount of overheadbits increases for larger number of models N and smaller tilesizes S. A balance thus needs to be struck between flexibil-ity and signaling overhead. A straightforward approach tothis problem would be to encode each frame using multiplevalues of S and N . This was deemed to be computationallyprohibitive. Instead, S and N are determined heuristicallybased on an extensive set of experiments using different val-ues for S and N while coding different sequences with vary-ing resolutions and targeting different quality levels. Theseexperiments have shown that tile sizes exceeding 16x16 lumasamples do not provide sufficient granularity to adapt to thedifferent regions, while 4x4 sample tiles represent the lowerbound as it is the smallest transform size in the H.264/AVCstandard. The following rules are used to determine S:S =8× 8 if Nmb ≤ 396 and QP ≥ 2816× 16 if Nmb ≤ 396 and QP < 284× 4 if Nmb > 396.(1)with Nmb denoting the number of macroblocks in the frameand QP denoting the average quantization parameter in theprevious slice of the same type.Our experiments also show that a value of N = 4 is uni-versally optimal except in the case of low-quality HD contentwhere it may be reduced to 3. N is thus selected as:N ={3 if Nmb > 396 and QP ≥ 284 otherwise.(2)Table 1. Bit rate savings for texture data of the proposedregion-adaptive extension to CABAC compared to regularCABAC, per cent.Type Sequence Proposedname vs. CABACCIFbus 0.84football 1.34foreman 0.71mobile 1.06paris 1.59stefan 0.65table 1.49720p HDtempete 0.73big ships 4.54city 3.21crew 3.79harbour 3.89night 4.015. EXPERIMENTAL RESULTSTable 1 presents bit savings, compared to classical CABAC,for a number of CIF (352× 288 pixels) and 1280× 720 reso-lution video sequences. The sequences are coded with I and Ppictures and a GOP size of 8. Quality parameter (QP) settingsare 20 for the CIF sequences and 30 for the 720p sequences.Bit savings for the CIF resolution test set measure up to 1.5 %.For the higher-resolution 720p sequences, particularly goodresults of 4 % on average are reported.6. CONCLUSIONWe presented a region-adaptive arithmetic coding method forvideo texture information. It reduces the impact on codingperformance of probability mis-estimation when transitingbetween image regions having different texture characteris-tics while not dramatically increasing computational com-plexity. Bit rate savings of 0.75 to 1.6 % for CIF sequencesand 3.5 to 4.5 % for 720p resolution sequences are obtained.7. REFERENCES[1] Thomas Wiegand, Gary J. Sullivan, Gisle Bjøntegaard,and Ajay Luthra, “Overview of the H.264/AVC videocoding standard,” IEEE Trans. Circuits Syst. Video Tech-nol., vol. 13, no. 7, pp. 560–576, 2003.[2] Detlev Marpe, Heiko Schwarz, and Thomas Wiegand,“Context-based Adaptive Binary Arithmetic Coding inthe H.264/AVC video compression standard,” IEEETrans. Circuits Syst. Video Technol., vol. 13, no. 7, pp.620–636, July 2003.[3] D. Hong, M. Van der Schaar, and B. Pesquet-Popescu,“Arithmetic coding with adaptive context-tree weightingfor the H.264 video coders,” in SPIE Visual Comm. andImage Proc. (VCIP), 2004.[4] M. H. Firooz and M. S. Sadri, “Improving H.264/AVCentropy coding engine using CTW method,” in 25th Pic-ture Coding Symposium, 2006.[5] D. Karwowski and M. Domanski, “Improved context-adaptive arithmetic coding in H.264/AVC,” in 17th Euro-pean Signal Proc. Conf (EUSIPCO), 2009.[6] E. Belyaev, M. Gilmutdinov, and A. Turlikov, “Binaryarithmetic coding system with adaptive probability esti-mation by virtual sliding window,” in Proc. Intl. Symp.for Consumer Electron. (ISCE), 2006.

Region-adaptive probability model selection for the arithmetic coding of video texture

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Region-adaptive probability model selection for the arithmetic coding of video texture

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