Predicting image quality using a modular image difference model

The paper is focused on the implementation of a modular color image difference model, as described in [1], with aim to predict visual magnitudes between pairs of uncompressed images and images compressed using lossy JPEG and JPEG 2000. The work involved programming each pre-processing step, processing each image file and deriving the error map, which was further reduced to a single metric. Three contrast sensitivity function implementations were tested; a Laplacian filter was implemented for spatial localization and the contrast masked-based local contrast enhancement method, suggested by Moroney, was used for local contrast detection. The error map was derived using the CIEDE2000 color difference formula on a pixel-by-pixel basis. A final single value was obtained by calculating the median value of the error map. This metric was finally tested against relative quality differences between original and compressed images, derived from psychophysical investigations on the same dataset. The outcomes revealed a grouping of images which was attributed to correlations between the busyness of the test scenes (defined as image property indicating the presence or absence of high frequencies) and different clustered results. In conclusion, a method for accounting for the amount of detail in test is required for a more accurate prediction of image quality

Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric

Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively