DVQ: A digital video quality metric based on human vision

The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (digital video quality) (A. B. Watson, in Human Vision, Visual Processing, and Digital Display VIII, Proc. SPIE 3299, 139- 147 (1998)). Here, we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the discrete cosine transform. It incorporates aspects of early visual pro- cessing, including light adaptation, luminance, and chromatic chan- nels; spatial and temporal filtering; spatial frequency channels; con- trast masking; and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers. © 2001 SPIE and IS&T. (DOI: 10.1117/1.1329896)

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

[2]  John Allnatt,et al.  Transmitted-picture assessment , 1983 .

[3]  M. Georgeson,et al.  Facilitation and masking of briefly presented gratings: Time-course and contrast dependence , 1987, Vision Research.

[4]  J. M. Foley,et al.  Human luminance pattern-vision mechanisms: masking experiments require a new model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  A B Watson,et al.  Perceptual-components architecture for digital video. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[6]  Shuichi Matsumoto,et al.  Picture quality assessment system by three-layered bottom-up noise weighting considering human visual perception , 1999 .

[7]  Andrew B. Watson,et al.  Toward a perceptual video-quality metric , 1998, Electronic Imaging.

[8]  Albert J. Ahumada,et al.  Improved detection model for DCT coefficient quantization , 1993, Electronic Imaging.

[9]  J. Lubin A human vision system model for objective picture quality measurements , 1997 .

[10]  Christian J. Van Den Branden Lambrecht Color moving pictures quality metric , 1996, ICIP.

[11]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[12]  Stephen Wolf Objective and Subjective Measures of MPEG Video Quality , 1996 .

[13]  Stephen D. Voran,et al.  Objective video quality assessment system based on human perception , 1993, Electronic Imaging.

[14]  A. Watson,et al.  Quest: A Bayesian adaptive psychometric method , 1983, Perception & psychophysics.

[15]  Jeffrey B. Mulligan,et al.  Design and performance of a digital video quality metric , 1999, Electronic Imaging.

[16]  J. M. Foley,et al.  Contrast masking in human vision. , 1980, Journal of the Optical Society of America.

[17]  H de Ridder,et al.  Continuous assessment of perceptual image quality. , 1995, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  Andrew B. Watson,et al.  Perceptual optimization of DCT color quantization matrices , 1994, Proceedings of 1st International Conference on Image Processing.