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A High-Throughput Binary Arithmetic coding Architecture for H.264/AVC CABAC

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2010年第9期E93A卷 1594-1604页

作者： Liu, Yizhong Song, Tian Shimamoto, Takashi Univ Tokushima Inst Sci & Technol Grad Sch Engn Tokushima 7708506 Japan

In this paper, we propose a high-throughput binary arithmetic coding architecture for CABAC (Context Adaptive Binary Arithmetic coding) which is one of the entropy coding tools used in the H.264/AVC main and high profiles. The full CABAC encoding functions, including binarization, context model selection, arithmetic encoding and bits generation, are implemented in this proposal. The binarization and context model selection are implemented in a proposed binarizer, in which a FIFO is used to pack the binarization results and output 4 bins in one clock. The arithmetic encoding and bits generation are implemented in a four-stage pipeline with the encoding ability of 4 bins/clock. In order to improve the processing speed, the context variables access and update for 4 bins are paralleled and the pipeline path is balanced. Also, because of the outstanding bits issue, a bits packing and generation strategy for 4 bins paralleled processing is proposed. After implemented in verilog-HDL and synthesized with Synopsys Design Compiler using 90 nm libraries, this proposal can work at the clock frequency of 250MHz and takes up about 58K standard cells, 3.2 Kbits register files and 27.6K bits ROM. The throughput of processing 1000 M bins per second can be achieved in this proposal for the HDTV applications.

关键词： H.264/AVC encoder CABAC entropy coding binary arithmetic coding VLSI

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Prefix coding Scheme Supporting Direct Access Without Auxiliary Space

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第12期35卷 12917-12931页

作者： Wang, Na Yan, Wei Jiang, Hao Lin, Sian-Jheng Han, Yunghsiang S. Univ Shanghai Sci & Technol Sch Optoelect Informat & Comp Engn Shanghai 200093 Peoples R China Huawei Technol Co Ltd Theory Lab Hefei 230000 Peoples R China Huawei Technol Co Ltd Theory Lab Hong Kong Peoples R China Alibaba Grp Hangzhou 311121 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

entropy coding is a widely used technique for lossless data compression. The entropy coding schemes supporting the direct access capability on the encoded stream have been investigated in recent years. However, all prior schemes require auxiliary space to support the direct access ability. This paper proposes a rearranging method for prefix codes to support a certain level of direct access to the encoded stream without requiring additional data space. Then, an efficient decoding algorithm is proposed based on lookup tables. The simulation results show that when the encoded stream does not allow additional space, the number of bits per access read of the proposed method is above two orders of magnitude less than the conventional method. In contrast, the alternative solution consumes at least one more bit per symbol on average than the proposed method to support direct access. This indicates that the proposed scheme can achieve a good trade-off between space usage and access performance. In addition, if a small amount of additional storage space is allowed (it is approximately 0.057% in the simulation), the number of bits per access read in our proposal can be significantly reduced by 90%.

关键词： And additional data space data compaction and compression direct access entropy coding prefix codes

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Asymmetric Learned Image Compression With Multi-Scale Residual Block, Importance Scaling, and Post-Quantization Filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第8期33卷 4309-4321页

作者： Fu, Haisheng Liang, Feng Liang, Jie Li, Binglin Zhang, Guohe Han, Jingning Xi An Jiao Tong Univ Sch Microelect Xian 710049 Peoples R China Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada Google Inc Mountain View CA 95054 USA

Recently, deep learning-based image compression has made significant progresses, and has achieved better rate-distortion (R-D) performance than the latest traditional method, H.266/VVC, in both MS-SSIM metric and the more challenging PSNR metric. However, a major problem is that the complexities of many leading learned schemes are too high. In this paper, we propose an efficient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art. First, we develop an improved multi-scale residual block (MSRB) that can expand the receptive field and capture global information more efficiently, which further reduces the spatial correlation of the latent representations. Second, an importance scaling network is introduced to directly scale the latents to achieve content-adaptive bit allocation without sending side information, which is more flexible than previous importance map methods. Third, we apply a post-quantization filter (PQF) to reduce the quantization error, motivated by the Sample Adaptive Offset (SAO) filter in video coding. Moreover, our experiments show that the performance of the system is less sensitive to the complexity of the decoder. Therefore, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only uses one stage of MSRB, which reduces the decoder complexity and still yields satisfactory performance. Experimental results show that compared to the state-of-the-art method, the encoding and decoding time of the proposed method are about 17 times faster, and the R-D performance is only reduced by about 1% on both Kodak and Tecnick-40 datasets, which is still better than H.266/VVC(4:4:4) and other leading learning-based methods. Our source code is publicly available at https://***/fengyurenpingsheng.

关键词： Learning-based image compression multi-scale residual block importance scaling post-quantization filter entropy coding

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Lossy predictive coding of SAR raw data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2003年第5期41卷 977-987页

作者： Magli, E Olmo, G Politecn Torino Dipartimento Elettron I-10129 Turin Italy

In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for, this signal. We show that, due to the statistical properties of the SAR signa, 1, an along-range linear predictor With few, taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs. quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods. based on transform-coding;fractional output bit-rates can, also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.

关键词： differential pulse code modulation (DPCM) entropy coding entropy-constrained quantization predictive coding synthetic aperture radar (SAR) raw data compression

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41.5-Tb/s Transmission Over 549 km of Field Deployed Fiber Using Throughput Optimized Probabilistic-Shaped 144QAM

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2019年第1期37卷 148-155页

作者： Ip, Ezra Huang, Yue-Kai Zhang, Shaoliang Xia, Tiejun J. Peterson, Daniel L. Wellbrock, Glenn Milione, Giovanni Wang, Ting Aono, Yoshiaki Tajima, Tsutomu NEC Labs Amer Inc Princeton NJ 08540 USA Verizon Richardson TX 75080 USA NEC Corp Ltd Converged Network Div Minato Ku Tokyo 1088001 Japan

We demonstrate high spectral efficiency transmission over 549 km of field deployed single-mode fiber using probabilistic-shaped 144QAM. We achieved 41.5 Tb/s over the C-band at a spectral efficiency of 9.02 b/s/Hz using 32-Gbaud channels at a channel spacing of 33.33 GHz, and 38.1 Tb/s at a spectral efficiency of 8.28 b/s/Hz using 48-Chaud channels at a channel spacing of 50 CHz. To the best of our knowledge, these are the highest total capacities and spectral efficiencies reported in a metro field environment using C-band only. In high spectral efficiency transmission, it is necessary to optimize back-to-back performance in order to maximize the link loss margin. Our results are enabled by the joint optimization of constellation shaping and coding overhead to minimize the gap to Shannon's capacity, transmitter- and receiver-side digital backpropagation, signal clipping optimization, and I/Q imbalance compensation.

关键词： Coherent optical systems entropy coding modulation coding network field experiment optical fiber communications probabilistic shaping quadrature amplitude modulation source coding

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Lossy-to-lossless 3D image coding through prior coefficient lookup tables

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INFORMATION SCIENCES 2013年 239卷 266-282页

作者： Auli-Llinas, Frances Marcellin, Michael W. Serra-Sagrista, Joan Bartrina-Rapesta, Joan Univ Autonoma Barcelona Dept Informat & Commun Engn Bellaterra 08193 Spain Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

This paper describes a low-complexity, high-efficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors. (C) 2013 Elsevier Inc. All rights reserved.

关键词： 3D image coding entropy coding Bitplane image coding JPEG2000

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Context quantization by kernel Fisher discriminant

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2006年第1期15卷 169-177页

作者： Xu, MT Wu, XL Fränti, P Univ Joensuu Dept Comp Sci FIN-80101 Joensuu Finland McMaster Univ Dept Elect & Comp Engn Hamilton ON L8G 4K1 Canada

Optimal context quantizers for minimum conditional entropy can be constructed by dynamic programming in the probability simplex space. The main difficulty, operationally, is the resulting complex quantizer mapping function in the context space, in which the conditional entropy coding is conducted. To overcome this difficulty, we propose new algorithms for designing context quantizers in the context space based on the multiclass Fisher discriminant and the kernel Fisher discriminant (KFD). In particular, the KFD can describe linearly nonseparable quantizer cells by projecting input context vectors onto a high-dimensional curve, in which these cells become better separable. The new algorithms outperform the previous linear Fisher discriminant method for context quantization. They approach the minimum empirical conditional entropy context quantizer designed in the probability simplex space, but with a practical implementation that employs a simple scalar quantizer mapping function rather than a large lookup table.

关键词： context quantization entropy coding Fisher discriminants image compression

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Causal Contextual Prediction for Learned Image Compression

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第4期32卷 2329-2341页

作者： Guo, Zongyu Zhang, Zhizheng Feng, Runsen Chen, Zhibo Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Peoples R China

Over the past several years, we have witnessed impressive progress in the field of learned image compression. Recent learned image codecs are commonly based on autoencoders, that first encode an image into low-dimensional latent representations and then decode them for reconstruction purposes. To capture spatial dependencies in the latent space, prior works exploit hyperprior and spatial context model to build an entropy model, which estimates the bit-rate for end-to-end rate-distortion optimization. However, such an entropy model is suboptimal from two aspects: (1) It fails to capture global-scope spatial correlations among the latents. (2) Cross-channel relationships of the latents remain unexplored. In this paper, we propose the concept of separate entropy coding to leverage a serial decoding process for causal contextual entropy prediction in the latent space. A causal context model is proposed that separates the latents across channels and makes use of channel-wise relationships to generate highly informative adjacent contexts. Furthermore, we propose a causal global prediction model to find global reference points for accurate predictions of undecoded points. Both these two models facilitate entropy estimation without the transmission of overhead. In addition, we further adopt a new group-separated attention module to build more powerful transform networks. Experimental results demonstrate that our full image compression model outperforms standard VVC/H.266 codec on Kodak dataset in terms of both PSNR and MS-SSIM, yielding the state-of-the-art rate-distortion performance.

关键词： Context modeling Predictive models Image coding entropy Correlation entropy coding Transforms Learned image compression causal context model causal global prediction improved entropy model

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Enhancing Context Models for Point Cloud Geometry Compression With Context Feature Residuals and Multi-Loss

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2024年第2期14卷 224-234页

作者： Sun, Chang Yuan, Hui Li, Shuai Lu, Xin Hamzaoui, Raouf Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res & Sc Jinan 250000 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England De Montfort Univ Sch Engn & Sustainable Dev Leicester LE1 9BH England

In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the probability distribution predicted by the context model as the loss function. However, this approach has two main weaknesses. First, the differences between contexts of different nodes are not significant, making it difficult for the context model to accurately predict the probability distribution of node occupancy. Second, as the one-hot encoding is not the actual probability distribution of node occupancy, the cross-entropy loss function is inaccurate. To address these problems, we propose a general structure that can enhance existing context models. We introduce the context feature residuals into the context model to amplify the differences between contexts. We also add a multi-layer perception branch, that uses the mean squared error between its output and node occupancy as a loss function to provide accurate gradients in backpropagation. We validate our method by showing that it can improve the performance of an octree-based model (OctAttention) and a voxel-based model (VoxelDNN) on the object point cloud datasets MPEG 8i and MVUB, as well as the LiDAR point cloud dataset SemanticKITTI.

关键词： Geometry point cloud compression context model entropy coding deep learning

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Field programmable gate array implementation of variable-bins high efficiency video coding CABAC decoder with path delay optimisation

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IET IMAGE PROCESSING 2019年第6期13卷 954-963页

作者： Menasri, Wahiba Skoudarli, Abdellah Belhadj, Aichouche Azzaz, Mohamed Salah USTHB Fac Elect & Informat BP 32 El Alia Bab Ezzouar Algiers Algiers Algeria Ecole Mil Polytech BP 17 Bordj El Bahri Algiers Algeria

Context-based adaptive binary arithmetic coding (CABAC) is a single operation mode for entropy coding in the last video coding standard high-efficiency video coding. For high-resolution applications, the throughput of one bin/cycle is not sufficient and it is a very challenging task to implement pipeline and/or parallel CABAC decoding architecture by simply adding more stages. Indeed, the tight data dependencies make it difficult to parallelise and cause it to be a throughput bottleneck for video decoding. Consequently, in order to improve the CABAC decoder throughput, parallel and pipeline architectures are used in authors' design. In this work, an algorithm-architecture adequation is proposed to implement a CABAC decoder on a field programmable gate array. Mainly, a new classification of 32 syntax elements is given to speed up the authors' solution. Furthermore, the context selection and modelling of regular syntax elements are studied, designed and implemented. Finally, a novel technique of memories rearrangement to reduce the critical path delay required to process each binary symbol is proposed. As a result, the implementation can process 2.2 bins/cycle when operated at 123.49 MHz and exhibits an improved high-throughput of 271.678 Mbins/s. The hardware architecture is coded using hardware description language and synthesised using ISE Xilinx tools targeting the Virtex4 platform.

关键词： arithmetic codes binary codes adaptive codes entropy codes field programmable gate arrays video coding decoding hardware description languages high-throughput field programmable gate array implementation variable-bins high efficiency video coding CABAC decoder path delay optimisation context-based adaptive binary arithmetic coding single operation mode entropy coding video coding standard high-efficiency video coding high-resolution applications bin/cycle CABAC decoding architecture throughput bottleneck video decoding CABAC decoder throughput pipeline architectures algorithm-architecture adequation critical path delay 2.2 bins/cycle frequency 123.49 MHz

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