Global Rate-Distortion Optimization-Based Rate Control for HEVC HDR Coding

Zhou, Mingliang; Wei, Xuekai; Wang, Shiqi; Fong, Chi-Keung; Wong, P.H.W.; Yuen, Wilson Y. F.

doi:10.1109/tcsvt.2019.2959807

Cited by 22 publications

(3 citation statements)

References 43 publications

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“…Meanwhile, the method proposed in [56] can achieve a BD-rate of 1.1, a BD-PSNR of 0.3 and a latency reduction of 0.2. For the method of [63], the BD-rate and BD-PSNR are 4.6 and 5.4, respectively, which are much higher than those of baseline HM16.19 coding algorithms, including [59] and [115]. When a recently proposed JND-based perceptual RC method for HEVC [66] is compared with [116] and [117], it can be seen that this method significantly reduces the bit rate while ensuring the subjective video quality.…”

Section: B Implementations At the Block Levelmentioning

confidence: 91%

“…Meddeb et al developed an advanced algorithm for video coding that separated the video content into regions of interest and regions of noninterest and increased the bit allocation for regions of interest while maintaining the overall bit rate. The works in [59], [62], [63] considered the issue of visual differences in HDR video and built an R-D model based on this issue. The works in [53], [64], [66] considered perceptual RC methods.…”

Section: B λ-Domain Methodsmentioning

confidence: 99%

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Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

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Video coding is a video compression technique that compresses the original video sequence to achieve a smaller archive file or a lower transmission bandwidth under constraints on the visual quality loss. Rate control (RC) plays a critical role in video coding. It can achieve stable stream output in practical applications, especially in real-time video applications such as video conferencing or game live streaming. Most RC algorithms directly or indirectly characterize the relationship between the bit rate (R) and quantization (Q) and then allocate bits for every coding unit to guarantee the global bit rate and video quality level. This paper comprehensively reviews the classic RC technologies used in international video standards of past generations, analyses the mathematical models and implementation mechanisms of various schemes, and compares the performance of recent state-of-the-art RC algorithms. Finally, we discuss future directions and new application areas for RC methods. We hope that this review can help support the development, implementation, and application of RC in new video coding standards.

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Section: B Implementations At the Block Levelmentioning

confidence: 91%

Section: B λ-Domain Methodsmentioning

confidence: 99%

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

Self Cite

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“…However, the algorithm was designed only for a low delay coding structure, which may have some difficulties applying it for RA configuration. Zhou et al [41] formulated a clip function of λ and QP for the CTU-level rate control by proposing a visual difference predictor model for a high dynamic range input video. Raufmehr et al designed a fuzzy rate controller-based for the scalable HEVC [42].…”

Section: B Some Efforts To Improve the Existing Rate Control Model In The Ra Configurationmentioning

confidence: 99%

Deep Convolutional Feature-Driven Rate Control for the HEVC Encoders

et al. 2021

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This work proposes a rate control model based on deep convolutional features to improve the video coding performance of the HEVC encoders under the random access (RA) configuration. The proposed algorithm extracts high-level features from the original and previous coded frames using a pretrained visual geometry group (VGG-16) model by considering characteristics of a different temporal layer for the RA configuration. Subsequently, R-λ parameters (alpha and beta), bit allocation, λ estimation, and quantization parameter decision at frame-level are formulated by utilizing the extracted high-level features to maintain video quality and bitrate accuracy control. In addition, bit allocation at the group-of-picture (GOP)-level rate control is proposed with perceptual-based thresholding to control smooth bitrates and visual quality between adjacent GOPs. The results verify that the proposed algorithm is efficient in coding performance and bit accuracy by keeping visual quality. Compared with the existing R-λ rate model in HM-16.20, the proposed models can achieve an average BD-rate gain of -4.39% and -8.74% in PSNR and MSSSIM metrics for the RA configuration, respectively.INDEX TERMS Deep neural network, high efficiency video coding (HEVC), perceptual video coding, rate control, video coding

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