Just Noticeable Distortion-Based Perceptual Rate Control in HEVC

Zhou, Mingliang; Wei, Xuekai; Kwong, Sam; Jia, Weijia; Fang, Bin

doi:10.1109/tip.2020.3004714

Cited by 44 publications

(13 citation statements)

References 39 publications

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“…The complexity is mainly from the original and reconstructed frame visual feature extraction under the VGG-16 network employment. However, the proposed algorithm complexity is still relatively similar to other existing perceptual rate control models [27] and [28]. With the same classes of test sequences used by the proposed algorithm, the algorithms in [27] and [28] require 15.2× and 15.3× more encoding time than the anchor algorithm.…”

Section: Complexity Performance Evaluationsmentioning

confidence: 75%

“…To this end, this work proposes a deep neural network (DNN) feature-based GOP-level and frame-level for the HEVC encoder by extending a perceptual adaptive quantization parameter (QP) selection algorithm [22]. There have been many attempts to leverage the capability of DNN for perceptual quality purposes [22][23][24][25][26][27][28]. A common conception is that the DNN-based extracted features are primarily implicated in capturing global characteristics of the input images appealing for adaption perceptual quality problems.…”

Section: Introductionmentioning

confidence: 99%

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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: Complexity Performance Evaluationsmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Feature-Driven Rate Control for the HEVC Encoders

et al. 2021

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“…In [26] and [27], the local texture, color and motion are used as the influencing factors of the saliency model to achieve the purpose of local low-pass filtering. Zhou et al proposed to embed the R-D model based on the pixel-wise as a weighting factor into the rate control framework to reduce the bit [28]. Zhang et al proposed a fast CU size decision based on the JND model for 3D-HEVC, the Coding Tree Block (CTB) feature was analyzed to early skip the non-promising partition depth and intra prediction mode [29].…”

Section: A Improve Coding Effeciency By Jndmentioning

confidence: 99%

Fast CU Partition Decision Strategy Based on Human Visual System Perceptual Quality

2021

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A fast Coding Unit (CU) partition decision strategy based on Human Visual System (HVS) perception quality is proposed in this paper. Considering that it is difficult for existing fast algorithms to further improve compression efficiency, perceptual coding technology has been tried to remove visual redundancy for achieving the purpose of reducing the bit rate on the basis of maintaining subjective visual quality. However, the existing perceptual coding model is still insufficient to reflect the characteristics of the HVS, which has limited improvement in coding efficiency, especially in fast algorithms. In this method, the characteristics of the limited human capacity for spatial-temporal resolution and the visual sensory memory are used to improve coding performance. First, the color complexity is used as a control factor to optimize the Just Noticeable Difference (JND) model to remove visual redundancy in a way that is more in line with the characteristics of visual perception. Second, a classification model of motion patterns based on human visual saliency is designed to provide a basis for CU classification, which effectively improves the coding accuracy. Finally, an offline Decision Tree (DT) classifier is designed based on the above model, and texture features are incorporated into the classifier as another key attribute to further reduce the computational complexity. The results of performance evaluation confirm that the proposed method achieves significantly improved coding performance compared with original Versatile Test Model (VTM). Compared with existing algorithms, our method not only improves coding efficiency, but also improves subjective visual quality.

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“…In order to further explore the compression efficiency of video, many perceptual video coding (PVC) schemes have been proposed [3], [7], [10]- [12], [17], [18], [21], [27], [28], [31], [33]. PVC is a method of improving the coding efficiency without degrading subjective visual quality, unlike the conventional video coding schemes that minimize the absolute error between an original video and its compressed video.…”

Section: A Preprocessing-based Video Coding Schemesmentioning

confidence: 99%

“…PVC is a method of improving the coding efficiency without degrading subjective visual quality, unlike the conventional video coding schemes that minimize the absolute error between an original video and its compressed video. PVC schemes can be categorized into three folds: (i) Perceptual quantization schemes [3], [12], [17], [18] are implemented with quantization processes in video encoders by adjusting the quantization levels of the transform coefficients of the residual; (ii) Perceptual rate control schemes [33] find the optimal solution to the minimization problem for PVC using the JND-based Rate-Distortion (R-D) model; (iii) Preprocessing-based video coding schemes [7], [10], [11], [21], [27], [28], [31] reduce the energy of input signals to increase the entropy-theoretic compression efficiency before encoding. Compared to the other two PVC approaches, the preprocessing-based approaches are advantageous since they can be applied to any video codec without its internal functional modifications.…”

Section: A Preprocessing-based Video Coding Schemesmentioning

confidence: 99%

Learning-Based JND-Directed HDR Video Preprocessing for Perceptually Lossless Compression With HEVC

Kim

2020

IEEE Access

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Just Noticeable Distortion-Based Perceptual Rate Control in HEVC

Cited by 44 publications

References 39 publications

Deep Convolutional Feature-Driven Rate Control for the HEVC Encoders

Deep Convolutional Feature-Driven Rate Control for the HEVC Encoders

Fast CU Partition Decision Strategy Based on Human Visual System Perceptual Quality

Learning-Based JND-Directed HDR Video Preprocessing for Perceptually Lossless Compression With HEVC

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