Image Quality Assessment Complemented with Visual Regions of Interest

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. This paper introduces an image quality index, visual region of interest weighted quality index (VroiWQI). The index integrates the notion of visual regions of interest with the measurement of structural distortion between the original image and the distorted image to effectively match with human visual system (HVS). The method evaluates the structural distortion between the original image and the distorted image to arrive at the quality index in a local region. These local indices are then weighted based on the visual interest of the corresponding region, characterized by entropy in the local region that emphasizes the texture variance. VroiWQI of the image is calculated by averaging these weighted indices, considering different subsets of visual regions with degree of interest greater than a threshold value. Test results show that VroiWQI correlates well with subjective scores

[1]  Bernd Girod,et al.  What's wrong with mean-squared error? , 1993 .

[2]  James Hu,et al.  DVQ: A digital video quality metric based on human vision , 2001 .

[3]  John W. Senders,et al.  Distribution of visual attention in static and dynamic displays , 1997, Electronic Imaging.

[4]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[5]  Jeffrey Lubin,et al.  A VISUAL DISCRIMINATION MODEL FOR IMAGING SYSTEM DESIGN AND EVALUATION , 1995 .

[6]  Zhou Wang,et al.  Image Quality Assessment: From Error Measurement to Structural Similarity , 2004 .

[7]  Stefan Winkler,et al.  Video Quality Experts Group: current results and future directions , 2000, Visual Communications and Image Processing.

[8]  John D. Villasenor,et al.  Visibility of wavelet quantization noise , 1997, IEEE Transactions on Image Processing.

[9]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[10]  Alan C. Bovik,et al.  41 OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[11]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[12]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[13]  J. Findlay The Visual Stimulus for Saccadic Eye Movements in Human Observers , 1980, Perception.

[14]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[15]  Thrasyvoulos N. Pappas,et al.  Perceptual criteria for image quality evaluation , 2005 .

[16]  Claudio M. Privitera,et al.  Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  A. Bovik,et al.  OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[18]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[19]  Jean-Bernard Martens,et al.  Quality asessment of coded images using numerical category scaling , 1995, Other Conferences.