NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION
Keywords:human visual system (HVS), image quality assessment (IQA), multiply distorted images, no-reference image quality assessment (NR-IQA)
Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available.
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