CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing
This addresses the need for reliable image quality assessment to protect against errors in processing, but it appears incremental as it builds on existing reduced-reference methods.
The paper tackled the problem of assessing perceptual quality in image processing by proposing the Combined Distances of Contrast Distributions (CD2) method, achieving excellent performance on image quality assessment benchmarks with only a small data and computation overhead.
The quality of visual input is very important for both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually image processing is applied freely and lacks redundancy regarding safety. We propose a novel image comparison method called the Combined Distances of Contrast Distributions (CD2) to protect against errors that arise during processing. Based on the distribution of image contrasts a new reduced-reference image quality assessment (IQA) method is introduced. By combining various distance functions excellent performance on IQA benchmarks is achieved with only a small data and computation overhead.