3D Video Quality Evaluation Using Left and Right Eye Segmentation
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Solution Overview
Problem
Existing video quality evaluation techniques are inadequate for accurately assessing 3D video quality, as they rely on averaging 2D video qualities of left and right eye videos, which fails to account for significant differences between them, leading to inaccurate 3D video quality calculations.
Innovation Solution
A video quality evaluation apparatus and method that separately calculates the quality of left and right eye videos using existing 2D video quality evaluation algorithms and applies model equations based on experimental results to derive accurate 3D video quality, classifying videos into high and low quality categories for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If 2D video quality evaluation algorithms are used to calculate average quality of left and right eye videos, then the evaluation process is simple, but the 3D video quality calculation becomes inaccurate when there is significant quality difference between left and right eye videos
Solution Approach 1:
The patent segments the 3D video quality evaluation into three distinct components: base video quality (higher quality eye video), sub-video quality (lower quality eye video), and differential quality. This segmentation allows for separate evaluation of each eye video's quality and their difference, enabling more accurate 3D quality assessment through the formula: 3D video quality = base video quality - weight × quality difference
Solution Approach 2:
The patent introduces a quality differential parameter to capture the difference between left and right eye video qualities. By changing from a simple average approach to a differential approach that accounts for quality differences, the system achieves more accurate 3D video quality measurement while maintaining computational efficiency through the derived formula
2Productivity
If video coding compression is applied to reduce information amount, then network and storage efficiency is improved, but video quality deterioration occurs due to block noise, blurring, and other artifacts
Solution Approach 1:
The patent replaces subjective human quality assessment with an automated objective evaluation system that uses algorithmic processing of video data. The system substitutes mechanical/computational analysis for human perception, enabling efficient quality measurement of compressed videos without requiring actual human viewing, thus maintaining productivity gains from compression while providing accurate quality assessment
3Measurement precision
If network bandwidth is increased to prevent data corruption and maintain quality, then video quality is maintained, but network cost and infrastructure complexity increase
Solution Approach 1:
The patent performs preliminary quality evaluation of videos before they are distributed through the network. By assessing video quality in advance using the objective evaluation algorithm, the system can identify quality issues before they reach users, eliminating the need for increased network bandwidth or infrastructure complexity to prevent quality degradation. The preliminary action allows for proactive quality management
Data Source
AI summary
A video quality evaluation apparatus for evaluating a video quality that a user experiences for a service in which a 3D video is used, the video quality evaluation apparatus including: a 2D video quality derivation unit configured to derive, from input 3D video data, a left eye video quality that is a quality of a left eye video that is included in the 3D video data and a right eye video quality that is a quality of a right eye video that is included in the 3D video data; and a 3D video quality derivation unit configured to derive a quality of the 3D video from the left eye video quality and the right eye video quality.