Chung-hyo Kim · 2008
(CABAC) is the major entropy-coding algorithm employed in H.264/AVC. Although the performance gain of H.264/AVC is mostly resulted from CABAC, it is difficult to achieve a fast decoder because the decoding algorithm is basically sequential. In this paper, a prediction scheme is proposed to enhance overall decoding performance by decoding two binary symbols at a time. A CABAC decoder based on the proposed prediction scheme improves the decoding performance by 24 % compared to conventional decoders. I.
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Pei Qiu · 2005
To solve the problem of high computational complexity and large memory space in contextbased adaptive binary arithmetic coding (CABAC) of the video coding standard H.264, a new simple encoder scheme was proposed. Theoretical analysis of CABAC entropy coding architecture yielded a formula for calculating the coding efficiency, and optimal quantization point sets for different probabilities were obtained from this formula. After adjusting the precision of the range and scaling factor in the multiplicationfree adaptive binary arithmetic coder in CABAC, the effects of these parameters and the quantization point sets on the coding efficiency and memory space for the CABAC entropy coding were investigated, and the new encoder scheme was then set up. The experimental results on different picture sequences under this scheme show that the memory space and computational complexity can be reduced greatly without much decrease of the coding efficiency. This scheme is efficient in the video communication where less memory space is provided.
Cited by 1
Christian Rohlfing, Thibaut Meyer, Jens Schneider et al. · 2023
A Python wrapper for Context-based Adaptive Binary Arithmetic Coding (CABAC), extracted from the Test Model (VTM) for Versatile Video Coding (VVC), is presented. Besides providing Python access to CABAC, two extensions are proposed: the probability estimation progress for each context can be traced, and sequences of integer values can be coded without designing a dedicated context model set.
Cited by 1
Marpe, D., Blättermann, G., Heising, G. et al. · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · 2002
A new entropy coding scheme for video compression is presented. Context models are utilized for efficient prediction of the coding symbols. A novel binary adaptive arithmetic coding technique is employed to match the conditional entropy of the coding symbols given the context model estimates. The adaptation is also employed to keep track of non-stationary symbol statistics. Our new approach has been integrated into the current ITU-T H.26L test model (TML) to demonstrate the performance gain. By using our new entropy coding scheme instead of the current one variable length code approach of the current TML, large bit-rate savings up to 32% can be achieved. As a remarkable outcome of our experiments, we observed that high gains are reached not only at high bit-rates, but also at very low rates.
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B. Kalyan Chakravarthy B. Keerthi Priya · Tuijin Jishu/Journal of Propulsion Technology · 2023
The most recent standard for encoding video, known as H.265/MPEG-HEVC, achieves a rise in coding efficiency that is fifty percent higher than that of its predecessor, known as H.264/MPEG-AVC. High-resolution video coding (such as 4K, 8K, etc.) has attracted considerable interest since it was anticipated that H.265/MPEG-HEVC will end up replacing practically all current H.264/MPEG-AVC codecs. Video compression is a crucial method, especially in this day and age where greater quality and quicker frame-rate video are in high demand. There are other video compression algorithms available today, but the H.265 standard is one of the most well-known and widely utilized. The standard's last stage is the construction of a binary arithmetic coding algorithm that employs arithmetic coding theory principles to accomplish efficient encoding of the produced information. Binary arithmetic coding (BAC) is an important component of modern video compression techniques. A novel BAC method is examined in this work to demonstrate the feasibility of breaking the dependency of bin-bin in the refreshing of range and low registers. It is suggested to create new dataflow. The design implementation is performed using the Modelsim tool.
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Praneel Kumar Peruru, K. Reddy Madhavi · International Journal of Basic and Applied Sciences · 2025
The rapid development of telemedicine and intelligent healthcare technologies necessitates high efficiency and accuracy in medical image compression methods for effective remote diagnostics and precise treatment. This paper introduces a new Context-Based Adaptive Binary Arithmetic Coding (CABAC) framework designed to specifically compress sensitive healthcare images, such as MRI, CT, and X-ray images. Like conventional techniques such as JPEG and JPEG2000, which can corrupt important diagnostic information through lossy compression, the proposed CABAC-based algorithm leverages the statistical and distinctive nature of medical images to adaptively model the context and optimize binary arithmetic coding of the images. Therefore, it leads to increased compression ratios while maintaining diagnostically important essential image quality. The CABAC framework combines preprocessing, binarization, statistical context modeling, and binary arithmetic coding to achieve more compression efficiency. Quantitative analyses of conventional datasets at The Cancer Imaging Archive (TCIA) demonstrate that the proposed method achieves a compression ratio of up to 15:1, surpassing the capacity of JPEG and JPEG2000. Moreover, the technique also guarantees large Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values, which reveal the high visual and structural quality of the decomposed medical images. Designed with computational efficiency in mind, the model is well-suited for integration into real-time telemedicine technologies, such as innovative healthcare systems featuring AI-capable diagnostics and IoT-enabled medical devices. This method provides a viable remedy for bandwidth optimization, as well as addressing storage needs and improving the accuracy of diagnostic tests, especially in technically limited environments.
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Kulkarni, Sharmila Sriranga · eScholarship (California Digital Library) · 2021
H.264/AVC is a popular video coding standard used in the fields of communication, video streaming and broadcasting. The H.264/AVC standard as specified in ITU-T — ISO/IEC has two methods of entropy coding, namely Context-based Adaptive Variable Length Coding (CAVLC) and Context-based Adaptive Binary Arithmetic Coding (CABAC). CABAC utilizes probability estimation to achieve a bit-rate reduction of 19% compared to CAVLC.In order to deal with the higher level of computational complexity of the CABAC entropy coding over the CAVLC coding, the CABAC algorithm is sometimes implemented in hardware to achieve real- time high resolution video coding. The CABAC algorithm can be broken into smaller tasks that can be performed independently. This makes the many-core processor array an appropriate option for the hardware implementation of the CABAC encoding algorithm. The independent tasks within the algorithm can be assigned to individual cores of the array. The KiloCore II, which this thesis uses for its hardware platform, contains hundreds of programmable processors and multiple 64kB shared memories per chip. Lanes of processors are constructed to perform the functions of the blocks within the CABAC.The aim of this thesis is to compare the throughput results with existing hardware and software implementations of CABAC and to show that the throughput, power and energy in the case of the KiloCore II is competitive. The CABAC algorithm was mapped on the KiloCore II array using the Project manager and Simulator platform. The total area occupied by the algorithm was 3.52 mm 2 in 32 nm technology with 64 cores and 177 routing links. The implementation achieved a throughput of 37 million bins per second at 1.1 V operating voltage and an energy of 34.37 µJ per individual bin at 0.8 V operating voltage.This implementation of the CABAC has an improvement of 57 times in throughput, when compared to the software implementation, that is, the JM software reference run on the Intel Xeon Processor E5-2680 v2. Despite being a fully-software implementation, the presented KiloCore design achieves a throughput within a factor of five when compared to hardware CABAC implementations scaled to the same 32 nm fabrication technology.
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Shu Wang, Zejie Kuang, Guohe Zhang et al. · 2019
The promotion and application of HEVC face a great problem: how to reduce the coding complexity while ensuring the video compression rate. In order to reduce the complexity of renormalization, a fast renormalization algorithm is designed for the entropy coding module. This algorithm can perform renormalization and bit output without complex cyclic processes. At the meanwhile, an appropriate hardware structure is proposed and realized for the algorithm. Experimental results show that the design can reduce the renormalized frequency of 20.8%-25.6% and save the encoding time of 13.6%-30.9%. Moreover, the encoding rate can be up to 89 Mbin/s.
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Detlev Marpe, Heiko Schwarz, Thomas Wiegand · IEEE Transactions on Circuits and Systems for Video Technology · 2003
Context-based adaptive binary arithmetic coding (CABAC) as a normative part of the new ITU-T/ISO/IEC standard H.264/AVC for video compression is presented. By combining an adaptive binary arithmetic coding technique with context modeling, a high degree of adaptation and redundancy reduction is achieved. The CABAC framework also includes a novel low-complexity method for binary arithmetic coding and probability estimation that is well suited for efficient hardware and software implementations. CABAC significantly outperforms the baseline entropy coding method of H.264/AVC for the typical area of envisaged target applications. For a set of test sequences representing typical material used in broadcast applications and for a range of acceptable video quality of about 30 to 38 dB, average bit-rate savings of 9%-14% are achieved.
Cited by 1,212
Yan Su, Xie Cheng Jun · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
An algorithm of combining LZC and arithmetic coding algorithm for image compression is presented and both theory deduction and simulation result prove the correctness and feasibility of the algorithm. According to the characteristic of context-based adaptive binary arithmetic coding and entropy, LZC was modified to cooperate the optimized piecewise arithmetic coding, this algorithm improved the compression ratio without any additional time consumption compared to traditional method.
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Po-Sheng Liu, Jianwen Chen, Youn-Long Lin · 2007
We propose a full hardwired context-based adaptive binary arithmetic encoder for H.264/AVC. Our architecture includes a 14-way context pair generator, a 3-stage pipelined circuit for getting neighboring data and a 4-stage pipelined multiple-mode arithmetic encoder. The context pair generator is composed of binarization and context modeling and it can operate with arithmetic encoder concurrently. The arithmetic encoder can process one bin per cycle. Our whole CABAC encoder is able to process 0.67 bins per cycle on the average. Its performance is adequate for 1080 p (HDTV) resolution at 30 fps when running at 60 MHz.
Cited by 28
Szu-Wei Lee, C.‐C. Jay Kuo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
One way to save the power consumption in the H.264 decoder is for the H.264 encoder to generate decoderfriendly bit streams. By following this idea, a decoding complexity model of context-based adaptive binary arithmetic coding (CABAC) for H.264/AVC is investigated in this research. Since different coding modes will have an impact on the number of quantized transformed coeffcients (QTCs) and motion vectors (MVs) and, consequently, the complexity of entropy decoding, the encoder with a complexity model can estimate the complexity of entropy decoding and choose the best coding mode to yield the best tradeoff between the rate, distortion and decoding complexity performance. The complexity model consists of two parts: one for source data (i.e. QTCs) and the other for header data (i.e. the macro-block (MB) type and MVs). Thus, the proposed CABAC decoding complexity model of a MB is a function of QTCs and associated MVs, which is verified experimentally. The proposed CABAC decoding complexity model can provide good estimation results for variant bit streams. Practical applications of this complexity model will also be discussed.
Cited by 6
Detlev Marpe, Heiko Schwarz, Thomas Wieg · 2013
(CABAC) as a normative part of the new ITU-T/ISO/IEC standard H.264/AVC for video compression is presented. By combining an adaptive binary arithmetic coding technique with context modeling, a high degree of adaptation and redundancy reduction is achieved. The CABAC framework also includes a novel low-complexity method for binary arithmetic coding and probability estimation that is well suited for efficient hardware and software implementations. CABAC significantly outperforms the baseline entropy coding method of H.264/AVC for the typical area of envisaged target applications. For a set of test sequences representing typical material used in broadcast applications and for a range of acceptable video quality of about 30 to 38 dB, average bit-rate savings of 9%–14 % are achieved. Index Terms—Binary arithmetic coding, CABAC, context modeling, entropy coding, H.264, MPEG-4 AVC. I.
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Senior Member · 2016
Abstract—Context-adaptive binary arithmetic coding is a widespread technique in the field of image and video coding. Most state-of-the-art arithmetic coders produce a (long) codeword of a priori unknown length. Its generation requires a renormalization procedure to permit progressive processing. This paper intro-duces two arithmetic coders that produce multiple codewords of fixed length. Contrarily to the traditional approach, the genera-tion of fixed-length codewords does not require renormalization since the whole interval arithmetic is stored in the coder’s internal registers. The proposed coders employ a new context-adaptive mechanism based on variable-size sliding window that estimates with high precision the probability of the symbols coded. Their integration in coding systems is straightforward as demonstrated within the framework of JPEG2000. Experimental tests indicate that the proposed coders are computationally simpler than the MQ coder of JPEG2000 and the M coder of HEVC while achieving superior coding efficiency. Index Terms—Context-adaptive binary arithmetic coding, fixed-length arithmetic codes.
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Ioan Tabus, R. Mathew, P. Zanuttigh et al. · 2015
[5] J. Heo and Y.-S. Ho, “Improved context-based adaptive binary arithmetic coding over H.264/AVC for lossless depth map coding, ” IEEE Signal Processing
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Thinh M. Le · 2015
has been introduced to improve coding efficiency and network friendliness. Adopted for the main and higher profiles of H.264/AVC, the Context-based Adaptive Binary Arithmetic Coding (CABAC) entropy coding tool, combined with the Rate-Distortion Optimization (RDO) tool significantly improve video coding efficiency. However, high computational complexity and power consumption prevent SW implementation of CABAC in real-time and high compression video applications. The project aims at designing an efficient, SoC-based CABAC encoder which consists of three major functional blocks: binarizer, context modeler, and binary arithmetic coder (BAC) (Fig. 1b).
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Asma Ben Hmida, Salah Dhahri, Abdelkrim Zitouni · 2014
The CABAC (Context Adaptive Binary Arithmetic Coding) in the H.264/AVC standard consists of binarizer, arithmetic encoder, and bit generator. This paper presents hardware architecture design of the binarizer part of the CABAC (Context-Based Adaptive Binary Arithmetic Coding) entropy encoder as defined in the H.264/AVC video compression standard. The proposed architecture avoids the support of all the binarizer method. The proposed architecture avoids the support of all the binarizer method. After implemented in Verilog-HDL and synthesized with Xilinx ISE Design the proposed architecture consumes about 394 slices and can operate at frequencies up to 267 MHz.
Cited by 4
Donia Ammous Damak · SPIRE - Sciences Po Institutional REpository · 2017
This thesis consists mainly of two parts. The first is a comparative study ofthe two entropy coders "Context adaptive binary arithmetic coding" (CABAC) and"Context adaptive variable length coding" (CAVLC). The second is a contribution tothe development of a lossless video compression technique based on a modification of thestandard H.264\AVC.
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Lucas Silva Lopes, Philip A. Chou, Ricardo L. de Queiroz · Greater South Information System · 2022
Arithmetic coding is used in most media compression methods. Context modeling is usually done through frequency counting and look-up tables (LUTs). For long-memory signals, probability modeling with large context sizes is often infeasible. Recently, neural networks have been used to model probabilities of large contexts in order to drive arithmetic coders. These neural networks have been trained offline. We introduce an online method for training a perceptron-based context-adaptive arithmetic coder on-the-fly, called adaptive perceptron coding , which continuously learns the context probabilities and quickly converges to the signal statistics. We test adaptive perceptron coding over a binary image database, with results always exceeding the performance of LUT-based methods for large context sizes and of recurrent neural networks. We also compare the method to a version requiring offline training, which leads to equally satisfactory results.
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Lucas Lopes, Philip A. Chou, Ricardo L. de Queiroz · 2022
<p>Arithmetic coding is used in most media compression methods. Context modeling is usually done through frequency counting and look-up tables (LUTs). For long-memory signals, probability modeling with large context sizes is often infeasible. Recently, neural networks have been used to model probabilities of large contexts in order to drive arithmetic coders. These neural networks have been trained offline. We introduce an online method for training a perceptron-based context-adaptive arithmetic coder on-the-fly, called adaptive perceptron coding, which continuously learns the context probabilities and quickly converges to the signal statistics.We test adaptive perceptron coding over a binary image database, with results always exceeding the performance of LUT-based methods for large context sizes and of recurrent neural networks. We also compare the method to a version requiring offline training, which leads to equally satisfactory results.</p>
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