In order to accommodate the wide range of applications and the corresponding platforms where the H.264/AVC standard is currently in place, one should be able to optimize the encoder's computational complexity with...
详细信息
ISBN:
(纸本)9781612843490
In order to accommodate the wide range of applications and the corresponding platforms where the H.264/AVC standard is currently in place, one should be able to optimize the encoder's computational complexity with a careful selection of the coding configuration parameters. Motion estimation is the most time-consuming part of the encoder which constitutes up to 75% of the computational complexity. In this paper, the optimum selection of configuration parameters, including search range, reference frame, degree of down-sampling and number of truncation bits have been analyzed for the VLSI implementation of integer motion estimation in terms of distortion-complexity performance. Furthermore, the optimum parameter sets have been presented for different video sizes and different constraints on computational power.
Learning-based image coding has attracted increasing attentions for its higher compression efficiency than reigning image codecs. However, most existing learning-based codecs do not support variable rates with a singl...
详细信息
ISBN:
(纸本)9798400701085
Learning-based image coding has attracted increasing attentions for its higher compression efficiency than reigning image codecs. However, most existing learning-based codecs do not support variable rates with a single encoder;their decoders are also of fixed, high computational complexity. In this paper, we propose an End-to-end, Learning-based and Flexible Image Codec (ELFIC) that supports variable rate and flexible decoding complexity. First, we propose a general image codec with Nonlinear Feature Fusion Transform (NFFT) as nonlinear transforms to improve its rate-distortion (RD) performance. Second, we propose an Instance-aware Decoding complexity Allocation (IDCA) approach, which exploits image contents for a tradeoff between reconstruction quality and computational complexity in the decoding process. Third, we propose an RD-complexity (RDC) optimization algorithm, which maximizes the image quality under given rate and complexity constraints for the whole framework. Experimental results show that ELFIC achieves variable rate, flexible decoding complexity with the state-of-the-art RD performance. It also supports a more efficient decoding process by focusing on image contents. Source codes are available at https://***/Zhichen-Zhang/ELFIC-Image-Compression.
The most recent video coding standard, named Versatile Video Coding (VVC), greatly improved the compression rate compared to its predecessor, High Efficiency Video Coding (HEVC) using some new coding tools. Though the...
详细信息
ISBN:
(纸本)9781665412414
The most recent video coding standard, named Versatile Video Coding (VVC), greatly improved the compression rate compared to its predecessor, High Efficiency Video Coding (HEVC) using some new coding tools. Though these new option provide appreciable coding gain, its computational complexity is relatively high since the performance of these coding tools need to be evaluated for each Coding Tree Units (CTU) through the rate-distortionoptimization (RDO) process. To address this issue, in this paper, first, the effectiveness of the coding tools in various parts of the frame, such as the borderline and central CTU, is investigated. The results of this study show that the coding efficiency of some of these coding tools is much higher for the borderline CTUs due to their specific features. Hence, these coding tools would be only considered enable for the borderline CTUs in rate-distortion process to decrease the computational complexity, without affecting the coding gain considerably. Simulation results show that using this method, the compression efficiency decreased only by 0.64% in average, but the computational complexity is reduced considerably, by 28.31%, in average.
Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significantly longer decoding times, which hind...
详细信息
Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significantly longer decoding times, which hinders the practical applications of neural image codecs. This issue is especially pronounced when employing an effective yet time-consuming autoregressive context model since it would increase entropy decoding time by orders of magnitude. In this paper, unlike most previous works that pursue optimal RD performance while temporally overlooking the coding complexity, we make a systematical investigation on the rate-distortioncomplexity (RDC) optimization in neural image compression. By quantifying the decoding complexity as a factor in the optimization goal, we are now able to precisely control the RDC trade-off and then demonstrate how the rate-distortion performance of neural image codecs could adapt to various complexity demands. Going beyond the investigation of RDC optimization, a variable-complexity neural codec is designed to leverage the spatial dependencies adaptively according to industrial demands, which supports fine-grained complexity adjustment by balancing the RDC tradeoff. By implementing this scheme in a powerful base model, we demonstrate the feasibility and flexibility of RDC optimization for neural image codecs.
The latest video compression standard, High Efficiency Video Coding (HEVC), has greatly improved the coding efficiency compared to the predecessor H. 264/AVC. However, equipped with the quadtree structure of coding tr...
详细信息
The latest video compression standard, High Efficiency Video Coding (HEVC), has greatly improved the coding efficiency compared to the predecessor H. 264/AVC. However, equipped with the quadtree structure of coding tree unit partition and other sophisticated coding tools, HEVC brings a significant increase in the computational complexity. To address this issue, a coding unit (CU) decision method based on fuzzy support vector machine (SVM) is proposed for rate-distortion-complexity (RDC) optimization, where the process of CU decision is formulated as a cascaded multi-level classification task. The optimal feature set is selected according to a defined misclassification cost and a risk area is introduced for an uncertain classification output. To further improve the RDC performance, different regulation parameters in SVM are adopted and outliers in training samples are eliminated. Additionally, the proposed CU decision method is incorporated into a joint RDC optimization framework, where the width of risk area is adaptively adjusted to allocate flexible computational complexity to different CUs, aiming at minimizing computational complexity under a configurable constraint in terms of RD performance degradation. Experimental results show that the proposed approach can reduce 58.9% and 55.3% computational complexity on average with the values of Bjonteggard delta peak-signal-to-noise ratio as -0.075 dB and -0.085 dB and the values of Bjontegaard delta bit rate as 2.859% and 2.671% under low delay P and random access configurations, respectively, which has outperformed the state-of-the-art fast algorithms based on statistical information and machine learning.
暂无评论