Foveated wavelet image quality index

The human visual system (HVS) is highly non-uniform in sampling, coding, processing and understanding. The spatial resolution of the HVS is highest around the point of fixation (foveation point) and decreases rapidly with increasing eccentricity. Currently, most image quality measurement methods are designed for uniform resolution images. These methods do not correlate well with the perceived foveated image quality. Wavelet analysis delivers a convenient way to simultaneously examine localized spatial as well as frequency information. We developed a new image quality metric called foveated wavelet image quality index (FWQI) in the wavelet transform domain. FWQI considers multiple factors of the HVS, including the spatial variance of the contrast sensitivity function, the spatial variance of the local visual cut-off frequency, the variance of human visual sensitivity in different wavelet subbands, and the influence of the viewing distance on the display resolution and the HVS features. FWQI can be employed for foveated region of interest (ROI) image coding and quality enhancement. We show its effectiveness by using it as a guide for optimal bit assignment of an embedded foveated image coding system. The coding system demonstrates very good coding performance and scalability in terms of foveated objective as well as subjective quality measurement.

[1]  S J Anderson,et al.  Peripheral spatial vision: limits imposed by optics, photoreceptors, and receptor pooling. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[2]  Wilson S. Geisler,et al.  Real-time foveated multiresolution system for low-bandwidth video communication , 1998, Electronic Imaging.

[3]  Junghoon Jung,et al.  Enhancement of region-of-interest coded images by using adaptive regularization , 2000, 2000 Digest of Technical Papers. International Conference on Consumer Electronics. Nineteenth in the Series (Cat. No.00CH37102).

[4]  Wilson S. Geisler,et al.  Visual detection following retinal damage: predictions of an inhomogeneous retino-cortical model , 1996, Photonics West.

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

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

[7]  Zhou Wang,et al.  Embedded foveation image coding , 2001, IEEE Trans. Image Process..

[8]  A. L. Yarbus Eye movements and vision Plenum Press , 1967 .

[9]  Ee-Chien Chang,et al.  Foveation Techniques and Scheduling Issues in Thinwire Visualization , 1998 .

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

[11]  A. L. I︠A︡rbus Eye Movements and Vision , 1967 .

[12]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[13]  Nariman Farvardin,et al.  Lossy/lossless region-of-interest image coding based on set partitioning in hierarchical trees , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[14]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.