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Information content weighting for perceptual image quality assessment
1Department of Electrical and Computer Engineering, Universityof Waterloo, Waterloo, ON, N2L 3G1, Canada. zhouwang@ieee.org
Summary
Pooling in image quality assessment (IQA) is improved by weighting local image information content. This approach enhances traditional metrics like peak signal-to-noise ratio and boosts overall IQA algorithm performance.
Area of Science:
- Computer Vision
- Signal Processing
- Perceptual Computing
Background:
- State-of-the-art image quality assessment (IQA) algorithms typically use a two-stage process: local quality measurement and pooling.
- The pooling stage often lacks theoretical grounding and reliable computational models, relying on ad-hoc methods.
Purpose of the Study:
- To investigate the hypothesis that optimal pooling weights in perceptual image quality assessment should correlate with local information content.
- To explore the use of advanced statistical models for estimating information content in natural images.
Main Methods:
- Utilized six publicly available subject-rated image databases for extensive studies.
- Estimated local information content in units of bits using advanced statistical models of natural images.
- Applied information content weighting to the pooling stage of IQA algorithms.
Main Results:
- Information content weighting consistently improved the performance of IQA algorithms.
- Peak signal-to-noise ratio (PSNR), when weighted by information content, became a competitive perceptual quality measure.
- Combining information content weighting with multiscale structural similarity measures yielded the best overall performance.
Conclusions:
- Perceptual weights for pooling in image quality assessment should be proportional to local information content.
- Information content weighting offers a theoretically grounded and effective approach to improve IQA algorithms.
- This method enhances existing metrics and provides a pathway to superior perceptual quality prediction.
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