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Generative Multi-Symbol Architecture of the Binary Arithmetic Coder for UHDTV Video Encoders

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2020年第3期67卷 891-902页

作者： Pastuszak, Grzegorz Warsaw Univ Technol Inst Radioelect & Multimedia Technol PL-00661 Warsaw Poland

Binary arithmetic coding is a key part of recent video compression standards. Its throughput is limited by the inherent dependencies existing in the algorithm. As a consequence, a higher bin parallelism leads to lower clock frequencies. This paper presents an architecture able to exceed limits existing in previous hardware implementations. The architecture exploits less probable symbols as starting points for long series of bins coded in one clock cycle. The evaluation for four possible cases of the range value allows its update in the pipeline before the delayed selection based on actual value. The adaptive division into series is proposed to make long series more frequent. To shorten critical paths, rMPS variables computed for symbols coded in the same clock cycle are first summed and then added to the low register. Up to 16 bypass-mode symbols can be processed in parallel with context-coded symbols in one clock cycle. The architecture is generative, i.e., its throughput can be scaled with resources without strict limits. For example, the binary arithmetic coder synthesized on 90nm TSMC technology which consumes 101.4k gates and operates at the 570 MHz has the average throughput of 13.4 bins per clock cycle for the high-quality H.265/HEVC compression.

关键词： CABAC binary arithmetic coding entropy coding video encoder VLSI FPGA UHDTV H264 AVC H265 HEVC

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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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aiWave: Volumetric Image Compression With 3-D Trained Affine Wavelet-Like Transform

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2023年第3期42卷 606-618页

作者： Xue, Dongmei Ma, Haichuan Li, Li Liu, Dong Xiong, Zhiwei Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & App Hefei 230027 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China

Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice. At present, the most commonly used volumetric image compression methods are based on wavelet transform, such as JP3D. However, JP3D employs an ideal, separable, global, and fixed wavelet basis to convert input images from pixel domain to frequency domain, which seriously limits its performance. In this paper, we first design a 3-D trained wavelet-like transform to enable signal-dependent and non-separable transform. Then, an affine wavelet basis is introduced to capture the various local correlations in different regions of volumetric images. Furthermore, we embed the proposed wavelet-like transform to an end-to-end compression framework called aiWave to enable an adaptive compression scheme for various datasets. Last but not least, we introduce the weight sharing strategies of the affine wavelet-like transform according to the volumetric data characteristics in the axial direction to reduce the number of parameters. The experimental results show that: 1) when cooperating our trained 3-D affine wavelet-like transform with a simple factorized entropy coding module, aiWave performs better than JP3D and is comparable in terms of encoding and decoding complexities;2) when adding a context module to remove signal redundancy further, aiWave can achieve a much better performance than HEVC.

关键词： Wavelet transforms Image coding Image reconstruction Bit rate Biomedical imaging Filtering entropy coding Volumetric image compression wavelet-like transform lifting scheme convolutional neural network end-to-end image compression

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On the minimum entropy of a mixture of unimodal and symmetric distributions

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IEEE TRANSACTIONS ON INFORMATION THEORY 2008年第7期54卷 3166-3174页

作者： Chen, Ting-Li Geman, Stuart Acad Sinica Inst Stat Sci Taipei 115 Taiwan Brown Univ Div Appl Math Providence RI 02912 USA

Progressive encoding of a signal generally involves an estimation step, designed to reduce the entropy of the residual of an observation over the entropy of the observation itself. Oftentimes the conditional distributions of an observation, given already-encoded observations, are well fit within a class of symmetric and unimodal distributions (e.g., the two-sided geometric distributions in images of natural scenes, or symmetric Paretian distributions in models of financial data). It is common practice to choose an estimator that centers, or aligns, the modes of the conditional distributions, since it is common sense that this will minimize the entropy, and hence the coding cost of the residuals. But with the exception of a special case, there has been no rigorous proof. Here we prove that the entropy of an arbitrary mixture of symmetric and unimodal distributions is minimized by aligning the modes. The result generalizes to unimodal and rotation-invariant distributions in R(n). We illustrate the result through some experiments with natural images.

关键词： entropy coding LOCO lossless image compression mixture distributions predictive coding symmetric distributions unimodal distributions

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Lossless compression of color mosaic images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2006年第6期15卷 1379-1388页

作者： Zhang, Ning Wu, Xiaolin McMaster Univ Dept Elect & Comp Engn Hamilton ON L8K 4K1 Canada

Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.

关键词： context quantization entropy coding digital camera image compression

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Hierarchical partition priority wavelet image compression

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1996年第7期5卷 1111-1123页

作者： Efstratiadis, SN Tzovaras, D Strintzis, MG Information Processing Laboratory Deptartment of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece

Image compression methods for progressive transmission using optimal hierarchical decomposition, partition priority coding (PPC), and multiple distribution entropy coding (MDEC) are presented, In the proposed coder, a hierarchical subband/wavelet decomposition transforms the original image. The analysis filter banks are selected to maximize the reproduction fidelity in each stage of progressive image transmission. An efficient triple-state differential pulse code modulation (DPCM) method is applied to the smoothed subband coefficients, and the corresponding prediction error is Lloyd-Max quantized. Such a quantizer is also designed to fit the characteristics of the detail transform coefficients in each subband, which are then coded using novel hierarchical PPC (HPPC) and predictive HPPC (PHPPC) algorithms, More specifically, given a suitable partitioning of their absolute range, the quantized detail coefficients are ordered based on both their decomposition level and partition and then are coded along with the corresponding address map, Space filling scanning further reduces the coding cost by providing a highly spatially correlated address map of the coefficients in each PPC partition. Finally, adaptive MDEC is applied to both the DPCM and HPPC/PHPPC outputs by considering a division of the source (quantized coefficients) into multiple subsources and adaptive arithmetic coding based on their corresponding histograms, Experimental results demonstrate the great performance of the proposed compression methods.

关键词： Image coding Pulse modulation entropy coding Wavelet transforms Image analysis Filter bank Image communication Modulation coding Algorithm design and analysis Partitioning algorithms

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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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High-Capacity Framework for Reversible Data Hiding Using Asymmetric Numeral Systems

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 8447-8461页

作者： Wang, Na Xu, Shuxi Qin, Chuan Lin, Sian-Jheng Shao, Shuo Han, Yunghsiang S. Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China East China Inst Optoelect Integrated Devices Bengbu 233000 Anhui Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Guangdong Peoples R China

Reversible data hiding (RDH) has been extensively studied in the field of multimedia security. Embedding capacity is an important metric for RDH performance evaluation. However, the embedding capacity of existing methods for independent and identically distributed (i.i.d.) gray-scale signals is still not good enough. In this paper, we propose a high-capacity RDH code construction method that employs asymmetric numeral systems (ANS) coding as the underlying coding framework. Based on the proposed framework, two RDH methods are presented. First, we propose a static RDH method that takes the constant host probability mass function (PMF) as input parameters and offers high embedding performance. Then, we give a dynamic RDH method that can eliminate the need for transmitting the host PMF in advance by designing a reversible dynamic probability calculator. The simulation results on discrete normally distributed signals demonstrate that the performance of the proposed static method is very close to the expected rate-distortion bound, and the proposed dynamic method can achieve satisfactory embedding capacity without prior knowledge of host PMF at the cost of slightly sacrificing steganographic data quality. Moreover, the experimental results on gray-scale images show that the proposed static method provides higher peak signal-to-noise ratio (PSNR) values and larger embedding capacities than some state-of-the-art methods, e.g., the embedding capacity of image Lena is as high as 3.571 bits per pixel.

关键词： Encoding Media Arithmetic Histograms Distortion Channel coding Gray-scale Asymmetric numeral systems embedding capacity entropy coding gray-scale signals reversible data hiding

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Communication-Efficient Federated Learning via Predictive coding

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 369-380页

作者： Yue, Kai Jin, Richeng Wong, Chau-Wai Dai, Huaiyu NC State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based compression scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compressed residual vector derived from the reference. In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding. Extensive simulations reveal that the communication cost can be reduced up to 99% with even better learning performance when compared with other baseline methods.

关键词： Predictive models Servers Collaborative work Predictive coding entropy coding Costs Quantization (signal) Federated learning distributed optimization predictive coding

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