Hierarchical temporal dependent rate-distortion optimization for low-delay coding

Gao, Yanbo; Zhu, Ce; Li, Shuai

doi:10.1109/iscas.2016.7527304

Cited by 12 publications

(17 citation statements)

References 4 publications

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“…For videos, many works [13]- [16], [24], [25] have been conducted to improve visual quality for the latest HEVC standard, aiming at improving the coding efficiency at the encoder side. The works of [24] and [25] propose to improve the HEVC quality by Rate-Distortion Optimization (RDO) at the HEVC encoder.…”

Section: A Related Work For Quality Enhancementmentioning

confidence: 99%

Enhancing Quality for HEVC Compressed Videos

Yang

et al. 2019

IEEE Trans. Circuits Syst. Video Technol.

174

115

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The latest High Efficiency Video Coding (HEVC) standard has been increasingly applied to generate video streams over the Internet. However, HEVC compressed videos may incur severe quality degradation, particularly at low bit-rates. Thus, it is necessary to enhance the visual quality of HEVC videos at the decoder side. To this end, this paper proposes a Quality Enhancement Convolutional Neural Network (QE-CNN) method that does not require any modification of the encoder to achieve quality enhancement for HEVC. In particular, our QE-CNN method learns QE-CNN-I and QE-CNN-P models to reduce the distortion of HEVC I and P/B frames, respectively. The proposed method differs from the existing CNN-based quality enhancement approaches, which only handle intra-coding distortion and are thus not suitable for P/B frames. Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P/B frames of HEVC videos. To apply our QE-CNN method in time-constrained scenarios, we further propose a Time-constrained Quality Enhancement Optimization (TQEO) scheme. Our TQEO scheme controls the computational time of QE-CNN to meet a target, meanwhile maximizing the quality enhancement. Next, the experimental results demonstrate the effectiveness of our TQEO scheme from the aspects of time control accuracy and quality enhancement under different time constraints. Finally, we design a prototype to implement our TQEO scheme in a real-time scenario.

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Section: A Related Work For Quality Enhancementmentioning

confidence: 99%

Enhancing Quality for HEVC Compressed Videos

Yang

et al. 2019

IEEE Trans. Circuits Syst. Video Technol.

174

115

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show abstract

“…The model proposed in [24] has been further extended by Xie et al in [25] for bit allocation strategy in the context of RC. Specific hierarchical coding schemes have also been investigated by Gao et al for Low Delay (LD) [26] and Random Access (RA) [27] coding configurations. In the specific case of HM and RA configuration, coding efficiency increases by 2.2% and can be further improved to 5.2% when the method is coupled with the high-complexity Multi Quantization Parameter (MQP) optimization proposed by Sullivan and Wiegand [7].…”

Section: B Global Rdo Solutionsmentioning

confidence: 99%

Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

Bichon¹,

Tanou²,

Ropert³

et al. 2019

IEEE Trans. on Image Process.

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Optimal adaptive quantization is one of the key points to optimize the coding efficiency of video encoders. Latest block-based video compression standards, such as High Efficiency Video Coding (HEVC), extensively use predictive coding techniques that create dependencies between blocks and increase the complexity of optimal block quantizers search. Specifically, the motion compensation is responsible for a dependency network connecting all blocks of the same GOP together. In this paper, this dependency network is estimated by a temporal distortion propagation model and an accurate estimation of Inter and Skip modes probabilities. Optimal quantizers are then designed per block in order to achieve the global optimization in terms of Rate-Distortion efficiency. By implementing the algorithm into the HEVC reference Model (HM), we report −16.51% PSNR-based and −26.26% SSIM-based average bitrate savings compared to no adaptive quantization. The proposed algorithm outperforms several related methods from state-of-the-art. Moreover, along with the demonstration of optimal quantizer solution, we propose an in-depth analysis of the algorithm behavior. This analysis includes, among others, the relative distribution of rates between frames and the control of quantizers dynamic range.

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“…However, only the direct dependency between the current CU and its reference (depth 1) is considered. In [6], a similar approach is extended for multiple-reference motion prediction and compensation, but again it only considers a "one-depth" dependency between blocks. A computationally complex proposal was made by Winken et al [7].…”

Section: Introductionmentioning

confidence: 99%

“…If we assume that the whole sequence is encoded with a unique quantizer ( = 2 −4 6 ) then the expression becomes:…”

mentioning

confidence: 99%

R-D spatio-temporal adaptive quantization based on temporal distortion backpropagation in HEVC

Ropert¹,

Tanou²,

Bichon³

et al. 2017

2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)

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Nowadays, Rate-Distortion Optimization (RDO) is commonly used in hybrid video coding to maximize coding efficiency. Usually, the rate distortion tradeoff is explicitly computed in offline encoder implementations whereas R(D) model are used in live encoders to select the best decisions at a lower computational cost. For sake of simplicity, this (mathematical) modelling is often performed for each coding unit (CU) individually and independently, obliterating the spatial or temporal dependency between CUs. In this paper, we provide a new spatio-temporal algorithm to compute local quantizers, based on a theoretical framework able to describe the temporal distortion propagation from an R-D standpoint. In particular, we model the temporal distortion propagation making possible the retro accumulations of any (spatial) psycho-visually weighted distortion onto reference images. Using the R(D) Shannon bound, its high bitrate approximation, and a Lagrange optimization, analytical solutions are obtained for the local quantizers and the Lagrange multiplier. The proposed algorithm shows-4.4% BD-BR SSIM gains in average over state-of-the art algorithm in HEVC, using the same SSIM-based psycho-visual function.

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Hierarchical temporal dependent rate-distortion optimization for low-delay coding

Cited by 12 publications

References 4 publications

Enhancing Quality for HEVC Compressed Videos

Enhancing Quality for HEVC Compressed Videos

Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

R-D spatio-temporal adaptive quantization based on temporal distortion backpropagation in HEVC

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