Content-weighted video quality assessment using a three-component image model

Abstract. Objective image and video quality measures play important roles in numerous image and video processing applications. In this work, we propose a new content-weighted method for fullreference (FR) video quality assessment using a three-component image model. Using the idea that different image regions have different perceptual significance relative to quality, we deploy a model that classifies image local regions according to their image gradient properties, then apply variable weights to structural similarity image index (SSIM) [and peak signal-to-noise ratio (PSNR)] scores according to region. A frame-based video quality assessment algorithm is thereby derived. Experimental results on the Video Quality Experts Group (VQEG) FR-TV Phase 1 test dataset show that the proposed algorithm outperforms existing video quality assessment

• Type: Journal
• Year: 2010
• Where: JEI
• Authors: Chaofeng Li, Alan Conrad Bovik
• Keywords: Image, JEI 2010, Three-component Image Model, Video Quality Assessment