Measurement of visual quality is of fundamental importance to many image and video processing applications. Traditionally, quality assessment metrics predict visual quality following the paradigm of modeling the known properties of the Human Visual System (HVS). The Structural Similarity (SSIM) index is a new approach which assumes that the HVS is highly adapted for extracting structural information from a scene and has been proved outperforms PSNR and many state-of-the-art HVS-based metrics. In this paper, we present a new metric for video quality assessment which selects local sampling regions according to their edginess and uses the SSIM index as local distortion measurement. We validate the performance of our metric by testing it on the VQEG Phase I dataset, and showed that our metric has a good correlation with perceived visual quality.
Video Quality Assessment Based on Edge Structural Similarity
2008 Congress on Image and Signal Processing ; 3 ; 445-448
2008-05-01
285888 byte
Conference paper
Electronic Resource
English
Method of image quality assessment based on human visual system and structural similarity
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