An effective and efficient visual quality index based on local edge gradients

The structural similarity index measure is a well known and widely used full reference visual quality index. In this paper we introduce a new full reference visual quality index based on local edges and edge gradients in the wavelet domain. The proposed metric corresponds better to human judgement and is more efficient, in terms of computational complexity, than the structural similarity index measure. Furthermore, the proposed metric is more efficient than other state of the art metrics and surpasses them for certain visual impairment classes.

[1]  Sheila S. Hemami,et al.  THE ROLE OF EDGE INFORMATION TO ESTIMATE THE PERCEIVED UTILITY OF NATURAL IMAGES , 2009 .

[2]  Yuukou Horita,et al.  Image Quality Evaluation Model Based on Local Features and Segmentation , 2006, 2006 International Conference on Image Processing.

[3]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[4]  Patrick Le Callet,et al.  Subjective quality assessment IRCCyN/IVC database , 2004 .

[5]  Sheila S. Hemami,et al.  Natural image utility assessment using image contours , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[6]  Min Wu,et al.  Security evaluation for communication-friendly encryption of multimedia , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[7]  Florent Autrusseau,et al.  Toward a simplified perceptual quality metric for watermarking applications , 2010, Electronic Imaging.

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

[9]  Alan C. Bovik,et al.  Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Shengcai Liao,et al.  Learning Multi-scale Block Local Binary Patterns for Face Recognition , 2007, ICB.

[11]  Yongdong Zhang,et al.  Visual security evaluation for video encryption , 2010, ACM Multimedia.

[12]  F. Wrba,et al.  Pit pattern classification using extended Local Binary Patterns , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.