Abstract
Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.
Highlights
IntroductionSince 2004, the Structural Similarity Index Measure (SSIM) [2] has been used as an image quality assessment (IQA) method, which reflects the perceptual change of information about the structure of objects in a scene
SuperPixel-based SIMilarity (SPSIM) was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI)
The good results obtained for SPSIM and its modifications mainly relate to images from the TID2008 and TID2013 databases
Summary
Since 2004, the Structural Similarity Index Measure (SSIM) [2] has been used as an IQA method, which reflects the perceptual change of information about the structure of objects in a scene This measure uses the idea of pixel inter-dependencies, which are adjacent to each other in image space. Image quality measures make comparisons of features extracted from pixels (e.g., MSE, PSNR) or rectangular patches (e.g., SSIM: Structural Similarity Index Measure). Such patches usually have no visual meaning, whereas superpixels, unlike artificially generated patches, have a visual meaning and are matched to the image content.
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