HDR Image Quality Assessment Using Perceptual Frequency Features
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Solution Overview
Problem
Current image quality assessment (IQA) methods designed for low dynamic range (LDR) images are not suitable for high dynamic range (HDR) images due to differences in data distribution, necessitating the development of effective IQA models that align with the human visual system (HVS) for accurate quality evaluation.
Innovation Solution
A Full-Reference (FR) Image Quality Assessment method using Local and Global Frequency feature-based Model (LGFM) that extracts frequency features with Gabor and Butterworth filters, transferring images to perceptual space, and performing similarity measurements and pooling to generate accurate IQA scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If LDR-based IQA methods are used for HDR images, then the assessment process is simple, but the assessment accuracy deteriorates due to data distribution differences
Solution Approach 1:
The patent transforms HDR images into perceptual space using luminance mapping functions that convert physical luminance values to perceptual luminance values. This parameter transformation aligns the HDR data distribution with HVS characteristics, enabling accurate quality assessment while maintaining methodological simplicity
Solution Approach 2:
The patent introduces perceptual space as an intermediary representation between HDR image space and quality assessment metrics. By mapping HDR images through perceptual luminance transformation, the system creates a bridge that allows LDR-based IQA methods to accurately evaluate HDR image quality without directly handling complex HDR data distributions
2Measurement precision
If frequency domain analysis is applied to HDR images, then structural detail assessment improves, but computational complexity increases
Solution Approach 1:
The patent decomposes the frequency analysis into separate horizontal and vertical Gabor filter operations, processing different spatial frequency components independently. This segmentation allows precise structural detail assessment through localized frequency feature extraction while reducing overall computational complexity by breaking down the complex 2D frequency transformation into manageable 1D operations
Solution Approach 2:
The patent extends traditional spatial domain IQA methods by incorporating frequency domain analysis through Gabor filters and Butterworth filters. This dimensional extension from spatial to frequency domain enables precise structural detail assessment by capturing frequency characteristics that are invisible in spatial domain alone, while the filters provide computational efficiency
Data Source
AI summary
A system and a method for assessing quality of a high-dynamic range (HDR) image. The system comprises a feature extraction module arranged to extract a plurality of frequency features on a pair of reference image and a distorted image generated based on the reference image; a comparison module arranged to compare a pair of feature maps obtained by processing the extracted frequency features on both the reference image and the distorted image; and a scoring module arrange to output an image quality assessment (IQA) score of the distorted image with reference to the reference image provided; wherein the plurality of frequency features are associated with sensitive information in a human visual system (HVS).