VMAF based quantization parameter prediction model for low resolution video coding

Vu, Tien Huu; Cong, Huy Phi; Sisouvong, Thipphaphone; HoangVan, Xiem; NguyenQuang, Sang; DoNgoc, Minh

doi:10.1109/atc55345.2022.9942982

Cited by 2 publications

(2 citation statements)

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“…Therefore, VMAF scores are grouped into nine groups, as described in Figure 6. It is assumed that there are n values of VMAF in the quality level i th as follows: In the second step, the Lagrange cost function J j i value corresponding to crf j th in the quality level i th is computed by the following equation: (23) where j ∈ {20, 21, ..., 45} and D j V MAF , λ j V MAF , R j V MAF are computed as shown in Equations ( 12)-( 19) depending on the type of frame I, P, or B. In the third step, a minimum J * i at quality level i th is selected as follows:…”

Section: Dataset Collecting and Labelingmentioning

confidence: 99%

“…However, both of these proposed methods only focus on improving the rate-distortion (RD) performance of encoder, while the stability of the video quality at frame-level is not considered. To overcome the drawbacks of the previous methods, we proposed a VMAF-based method to predict QP by using the CNN model in article [23]. However, this method is applied for the intra-mode encoding and for low-resolution video sequences.…”

Section: Introductionmentioning

confidence: 99%

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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: Dataset Collecting and Labelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%