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 ...
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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 introduces two arithmetic coders that produce multiple codewords of fixed length. Contrary to the traditional approach, the generation 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.
In H.264/AVC coders, the optimal mode decision for each coding unit is achieved based on a calculation of the rate-distortion (R-D) cost for each candidate mode. In this letter, we propose a context-adaptivebinary ar...
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In H.264/AVC coders, the optimal mode decision for each coding unit is achieved based on a calculation of the rate-distortion (R-D) cost for each candidate mode. In this letter, we propose a context-adaptive binary arithmetic coding (CABAC)-based rate estimation scheme that reduces the computational complexity of the R-D cost function evaluation. The complex and time-consuming calculation of the arithmeticcoding is replaced by a table lookup based on the proposed CABAC rate model for fast rate estimation. The context modeling part of CABAC is also simplified to accelerate the rate estimation process. The experimental results show that the proposed estimator follows the actual rate with the coefficient of determination R-2 = 0.9997. The proposed estimator yields about a 30% reduction in the computational complexity of the R-D optimized mode decision, thereby accelerating the entire encoder by about 17% to 25% with almost no degradation in the R-D performance.
In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which ...
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ISBN:
(纸本)9781538604625
In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which may restrict the compression efficiency. In this paper, we propose an arithmeticcoding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC. Instead of binarization, we propose to directly estimate the probability distribution of the 35 intra prediction modes with the adoption of a multi-level arithmetic codec. Instead of handcrafted context models, we utilize convolutional neural network (CNN) to perform the probability estimation. Simulation results show that our proposed arithmeticcoding leads to as high as 9.9% bits saving compared with CABAC.
High efficiency video coding (HEVC) seeks the best code tree configuration, the best prediction unit division and the prediction mode, by evaluating the rate-distortion functional in a recursive way and using a "...
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High efficiency video coding (HEVC) seeks the best code tree configuration, the best prediction unit division and the prediction mode, by evaluating the rate-distortion functional in a recursive way and using a "try all and select the best" strategy. Further, HEVC only supports contextadaptivebinaryarithmeticcoding (CABAC), which has the disadvantage of being highly sequential and having strong data dependencies, as the entropy coder. So, the development of a fast rate estimation algorithm for CABAC-based coding has a great practical significance for mode decision in HEVC. There are three elementary steps in CABAC encoding process: binarization, context modeling, and binaryarithmeticcoding. Typical approaches to fast CABAC rate estimation simplify or eliminate the last two steps, but leave the binarization step unchanged. To maximize the reduction of computational complexity, we propose a fast entropy-based CABAC rate estimator in this paper. It eliminates not only the modeling and the coding steps, but also the binarization step. Experimental results demonstrate that the proposed estimator is able to reduce the computational complexity of the mode decision in HEVC by 9-23 % with negligible PSNR loss and BD-rate increment, and therefore exhibits applicability to practical HEVC encoder implementation.
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