HDR Video Quality Assessment via Color Space Conversion
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Solution Overview
Problem
Current image and video technologies face challenges in assessing image quality over extended dynamic ranges and wide color gamuts, as existing methods struggle to accurately evaluate visual differences in high dynamic range and wide color gamut images, leading to inefficiencies in quality assessment and display.
Innovation Solution
The process involves converting video signals from one color space with a specific dynamic range and gamut to another, mapping color-related components over the new dynamic range, and processing these signals to measure differences, allowing for the assessment of visual quality characteristics based on the measured differences between the processed signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing quality assessment methods are used for HDR and WCG images, then the assessment process is simple, but the measurement precision of visual differences is insufficient
Solution Approach 1:
The quality assessment process is segmented into distinct stages: color space conversion, dynamic range mapping, signal processing, difference measurement, and visual quality assessment. This segmentation allows each stage to be optimized independently, improving overall measurement precision while managing complexity through modular organization of assessment operations
Solution Approach 2:
The method changes multiple parameters including color space coordinates, dynamic range values, and signal characteristics through conversion and mapping operations. These parameter transformations enable accurate representation of HDR and WCG image properties, significantly improving measurement precision of visual differences compared to traditional SDR assessment methods
2Manufacturing precision
If video signals are converted and mapped to extended dynamic ranges and wide color gamuts, then the representation accuracy of HDR and WCG images is improved, but the processing complexity increases
Solution Approach 1:
The system performs preliminary color space conversion and dynamic range mapping before quality assessment. By pre-processing the video signals to establish proper color relationships and dynamic range characteristics, the method ensures accurate representation of HDR and WCG images while simplifying subsequent assessment operations
Solution Approach 2:
The patent introduces intermediate processing steps including color space conversion and dynamic range mapping as mediators between the original video signals and the quality assessment process. These intermediary operations enable accurate representation of complex HDR and WCG image properties while providing a structured framework for managing processing complexity
3Measurement precision
If color-related components are mapped over extended dynamic ranges, then the visual quality characteristic assessment is improved, but the processing time and computational cost increase
Solution Approach 1:
The method applies selective mapping and processing to color-related components based on their importance to visual quality perception. By focusing computational resources on critical color and dynamic range parameters rather than processing all signal components equally, the system achieves accurate visual quality assessment while reducing overall processing time and computational cost
Data Source
AI summary
A first video signal is accessed, and represented in a first color space with a first color gamut, related to a first dynamic range. A second video signal is accessed, and represented in a second color space of a second color gamut, related to a second dynamic range. The first accessed video signal is converted to a video signal represented in the second color space. At least two color-related components of the converted video signal are mapped over the second dynamic range. The mapped video signal and the second accessed video signal are processed. Based on the processing, a difference is measured between the processed first and second video signals. A visual quality characteristic relates to a magnitude of the measured difference between the processed first and second video signals. The visual quality characteristic is assessed based, at least in part, on the measurement of the difference.