Model Parameter Estimation for CTU-Level Rate Control in HEVC

Li, Bo; Zhou, Mingliang; Zhang, Yongfei; Lin, Xupeng; Guo, Weihan

doi:10.1109/mmul.2018.112142602

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…Specifically, the proposed algorithm was evaluated under the common test conditions of HEVC [53] to assess bitrate accuracy, coding efficiency, objective visual quality under the peak signal-tonoise ratio (PSNR), and the multi-scale structural similarity (MSSSIM) metrics, and the subjective evaluation. We compared the proposed algorithm with other rate control algorithms in HM-16.20, initially from the R-λ model [2] and Li et al algorithm called model parameter estimation (MPE) [54] under the same experimental conditions.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Deep Convolutional Feature-Driven Rate Control for the HEVC Encoders

et al. 2021

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“…The subjective evaluation was performed by conducting the mean opinion scores (MOS) test and calculating the difference MOS (DMOS) scores. The assessments were completed by comparing the proposed algorithm with the HM-16.20, URQ model [17], OBA model [28], and MPE model [29].…”

Section: Resultsmentioning

confidence: 99%

“…Different initial values of α and β are claimed to have little impact on the compressed videos, R-D performance, and bitrate accuracy [19]. However, during the updating process, both model parameters are unable to fit the relationship between distortion and bitrate for each CTU [24][25][26][27][28][29][30] accurately. This is because, in general, the value is not equal to × ( ) .…”

Section: Current State Of R− Model For Ctu-level Rate Control Algmentioning

confidence: 99%

“…However, the proposed algorithm does not consider any initial QP scheme that leads to insignificant performance when sequences are coded with higher initial QP values. Li et al [29] proposed a model parameter estimation algorithm (MPE) for α and β for CTU-level rate control by analyzing the CTU scan order that correlates to the complexity of the CTU contents. However, the proposed algorithm does not examine the bit allocation, λ, and QP estimation models, which limits the MPE algorithm performance.…”

Section: Current State Of R− Model For Ctu-level Rate Control Algmentioning

confidence: 99%

“…Hence, the estimation process does not favor a proper R-λ relationship, and this leads to inaccurate rate control and failing to minimize distortion. Several studies have demonstrated that CTU-level rate control efficiency in HEVC can be achieved by diminishing the error due to the selected R-D model [24][25][26] and the error due to inaccurately estimating the model parameters [27][28][29][30]. In this study, the proposed algorithm establishes a new model for α and β estimations to improve the correlation between them and the estimated λ, based on the high-level features and QP values of previously coded CTUs.…”

Section: Introductionmentioning

confidence: 99%

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Optimal CTU-Level Rate Control Model for HEVC Based on Deep Convolutional Features

2020

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This paper proposes an optimal rate control model based on deep neural network (DNN) features to improve the coding tree unit (CTU)-level rate control in high-efficiency video coding for conversational videos. The proposed algorithm extracts high-level features from the original and previously reconstructed CTU blocks based on a predefined DNN model of the visual geometry group (VGG-16) network. Then, the correlation of the high-level feature and quantization parameter (QP) values of previously coded CTUs is explored for subjective visual characteristics to estimate the CTU-level rate control model parameters (alpha and beta) and the bit allocation of each CTU. Therefore, this paper also proposes a new model for Lambda estimation for each CTU by improving its relationship with the estimated bits per pixel to control the rate and relative distortion. Furthermore, the Lambda and QP boundary settings were adjusted based on the proposed perceptual model to ensure the rate control accuracy of each CTU. The results of experiments with the proposed algorithm, when compared to the rate control model in HM-16.20, reveal higher bitrate accuracy and an average BD-rate gain based on PSNR, SSIM, and MSSSIM metrics using the low-delay-P configuration. INDEX TERMS Deep neural network, high efficiency video coding (HEVC), rate control, video coding

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