Effects of Different Full-Reference Quality Assessment Metrics in End-to-End Deep Video Coding

Visual quality assessment is often used as a key performance indicator (KPI) to evaluate the performance of electronic devices. There exists a significant association between visual quality assessment and electronic devices. In this paper, we bring attention to alternative choices of perceptual loss...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2023-07, Vol.12 (14), p.3036
Main Authors: Xian, Weizhi, Chen, Bin, Fang, Bin, Guo, Kunyin, Liu, Jie, Shi, Ye, Wei, Xuekai
Format: Article
Language:English
Subjects:
Algorithms
Back propagation
Cameras
Coding
Electronic devices
Image coding
Mathematical optimization
Methods
Neural networks
Optimization
Performance evaluation
Quality assessment
Quality assurance
Sensors
Video compression
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Visual quality assessment is often used as a key performance indicator (KPI) to evaluate the performance of electronic devices. There exists a significant association between visual quality assessment and electronic devices. In this paper, we bring attention to alternative choices of perceptual loss function for end-to-end deep video coding (E2E-DVC), which can be used to reduce the amount of data generated by electronic sensors and other sources. Thus, we analyze the effects of different full-reference quality assessment (FR-QA) metrics on E2E-DVC. First, we select five optimization-suitable FR-QA metrics as perceptual objectives, which are differentiable and thus support back propagation, and use them to optimize an E2E-DVC model. Second, we analyze the rate–distortion (R-D) behaviors of an E2E-DVC model under different loss function optimizations. Third, we carry out subjective human perceptual tests on the reconstructed videos to show the performance of different FR-QA optimizations on subjective visual quality. This study reveals the effects of the competing FR-QA metrics on E2E-DVC and provides a guide for further future study on E2E-DVC in terms of perceptual loss function design.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12143036