A perceptual rate control algorithm based on luminance adaptation for HEVC encoders

Lim, Woong; Sim, Donggyu

doi:10.1007/s11760-019-01620-3

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…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. Zeng et al incorporated human visual acuity into the video coding process by separating the input video into perceptually sensitive areas and less perceptually sensitive areas and increasing the bit allocation for perceptually sensitive areas.…”

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

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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 λ-Domain Methodsmentioning

confidence: 99%

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

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For the CTU-level rate control, there have also been many works attempting to improve the rate control performance of the HEVC encoders by considering the perceptual characteristics of CTU blocks. Woong et al [39] proposed a luminance adaptation characteristic based on a pixel domain just noticeable difference model to determine bit allocation and QP value for each CTU in a frame. However, the model tends to have a similar performance with the anchor R-λ rate control model.…”

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

Self Cite

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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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“…Appropriately luminous displays are a primary requirement in providing a comfortable experience to the user 1 . To set the proper luminance of a display, adaptation must be considered, as the brightness of the display differs based on it 2,3 . For example, when we see a smartphone in a dark room, it seems too bright due to the change in adaptation from the ambient light to the dark room, even though the luminance of the display remains the same as that under ambient light.…”

Section: Introductionmentioning

confidence: 99%

“…1 To set the proper luminance of a display, adaptation must be considered, as the brightness of the display differs based on it. 2,3 For example, when we see a smartphone in a dark room, it seems too bright due to the change in adaptation from the ambient light to the dark room, even though the luminance of the display remains the same as that under ambient light. Thus, studies on the proper luminance depending on the adapted luminance have been conducted, [4][5][6][7] and an automatic luminance control system to provide suitable brightness of display has already been developed in smartphones.…”

Section: Introductionmentioning

confidence: 99%

Prediction model for discomfort luminance levels of head‐mounted displays

Kwak

2022

Color Research & Application

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To predict the luminance threshold of head‐mounted displays (HMDs), which can cause discomfort due to excessive brightness, a psychophysical experiment was conducted with 20 participants through a yes/no task under a wide luminance range (2–284 cd/m2). The participants were asked to adapt to the luminance of the HMD and further answer whether the test luminance of the HMD caused discomfort. The discomfort threshold was determined for each individual at each adaptation luminance, and a prediction model was proposed based on the mean value of each participant's discomfort luminance level. The proposed model represents that individuals may experience discomfort using an HMD with a luminance of about 280 cd/m2, regardless of whether they are provided time to adapt to the luminance for 2 min.

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A perceptual rate control algorithm based on luminance adaptation for HEVC encoders

Cited by 13 publications

References 23 publications

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

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

Deep Convolutional Feature-Driven Rate Control for the HEVC Encoders

Prediction model for discomfort luminance levels of head‐mounted displays

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