A No-reference Quality Assessment Algorithm for JPEG2000-compressed Images Based on Local Sharpness

摘要

In this paper, we present a no-reference quality assessment algorithm for JPEG2000-compressed images called EDIQ (EDge-based Image Quality). The algorithm works based on the assumption that the quality of JPEG2000-compressed images can be evaluated by separately computing the quality of the edgeear-edge regions and the non-edge regions where no edges are present. EDIQ first separates the input image into edgeear-edge regions and non-edge regions by applying Canny edge detection and edge-pixel dilation. Our previous sharpness algorithm, FISH [Vu and Chandler, 2012], is used to generate a sharpness map. The part of the sharpness map corresponding to the non-edge regions is collapsed by using root mean square to yield the image quality index of the non-edge regions. The other part of the sharpness map, which corresponds to the edgeear-edge regions, is weighted by the local RMS contrast and the local slope of magnitude spectrum to yield an enhanced quality map, which is then collapsed into the quality index of the edgeear-edge regions. These two indices are combined by a geometric mean to yield a quality indicator of the input image. Testing on the JPEG2000-compressed subsets of four different image-quality databases demonstrate that EDIQ is competitive with other no-reference image quality algorithms on JPEG2000-compressed images.