A VMAF Directed Perceptual Rate Distortion Optimization for Video Coding

Tong, Xun; Zhu, Chen; Xie, Rong; Xiong, Jian; Song, Li

doi:10.1109/bmsb49480.2020.9379915

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Because VMAF is considered as a perceptual distortion metric better than PSNR and SSIM, Ref. [29] proposed a method using VMAF to replace objective metrics in RDO. In particular, VMAF is estimated as a function of some visual factors, including brightness adaptability, texture complexity, contrast masking, and timing masking.…”

Section: Rdo Modelingmentioning

confidence: 99%

Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video Coding

Vu,

Do,

Nguyen

et al. 2023

Electronics

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In the industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named video multimethod assessment fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain consistent quality while still achieving high compression performance. First of all, in order to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of rate-distortion optimization (RDO) mechanism is adaptively computed according to rate-QP and quality-QP models. The experimental results show that the proposed PVC solution has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and the relevant benchmark, notably with around 10% bitrate saving on average.

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Section: Rdo Modelingmentioning

confidence: 99%

Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video Coding

Vu,

Do,

Nguyen

et al. 2023

Electronics

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show abstract

“…Video visual quality is an area closely related to video compression quality. It is a concept based on the Human Visual System (HVS) [1] . The human eye can receive and analyze images, i.e.…”

Section: Introductionmentioning

confidence: 99%

VVC adaptive QP offset algorithm based on visual perception

Jin,

Guan,

Liu

2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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The latest video coding standard, the Universal Video Coding Standard (VVC), uses new coding tools to greatly improve compression efficiency. However, the adaptive QP module in the coding framework ignores the characteristics of the human visual system HVS, resulting in coding results that differ from the real perception of the human eye. To incorporate visual perception into QP selection, this paper proposes an adaptive QP offset algorithm based on visual perception metrics. The first step is to design a QP offset algorithm based on the luminance, frequency, and time domain characteristics of the HVS. The visual perception index is designed to reflect the real perception of human eyes, and then the index is used to guide the CTU into different levels according to visual sensitivity. Experiments show that the algorithm achieves 0.74% performance improvement in VMAF - based BD-Rate and about 8% improvement in VMAF score compared to the original VVC.

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A Saliency Map Approach to Optimize VMAF for Video and Image Compression

Zheng,

Han,

2024

2024 Data Compression Conference (DCC)

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A VMAF Directed Perceptual Rate Distortion Optimization for Video Coding

Cited by 5 publications

References 13 publications

Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video Coding

Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video Coding

VVC adaptive QP offset algorithm based on visual perception

A Saliency Map Approach to Optimize VMAF for Video and Image Compression

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