Gradual Chroma Reduction and High-Level Visual Masking in Videos

This study investigates the inability of the human vision system to detect gradual changes in video quality, specifically chroma reduction. We present the results from a subjective study, in which participants compared video stimuli of three different types: pristine, mildly chroma-reduced background with a fixed rate, chroma reduced with a gradual rate from a pristine first frame to a fully achromatic background in the last one. Our results show that the second type yields quality ratings that are significantly lower than those of the gradually modified samples, which are on par with those of the pristine samples.

[1]  Steven Le Moan,et al.  Exploiting Change Blindness for Image Compression , 2015, 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[2]  Teresa H. Y. Meng,et al.  Color quantization of images based on human vision perception , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  D. Foster Color constancy , 2011, Vision Research.

[4]  Nader Mohsenian,et al.  An object-based approach to color subsampling , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[5]  Marc Ebner,et al.  Color Constancy , 2007, Computer Vision, A Reference Guide.

[6]  Karl R Gegenfurtner,et al.  A Bayesian Model of the Memory Colour Effect , 2018, i-Perception.

[7]  Marcus Barkowsky,et al.  Effect of content features on short-term video quality in the visual periphery , 2016, 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP).

[8]  Michael A. Cohen,et al.  What is the Bandwidth of Perceptual Experience? , 2016, Trends in Cognitive Sciences.

[9]  Geoffrey L. Cohen,et al.  Current Directions in Psychological Science , 2009 .

[10]  G. Underwood,et al.  Low-level visual saliency does not predict change detection in natural scenes. , 2007, Journal of vision.

[11]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

[12]  Ralph R. Martin,et al.  Efficient, Edge-Aware, Combined Color Quantization and Dithering , 2016, IEEE Transactions on Image Processing.

[13]  D. Simons,et al.  Change Blindness in the Absence of a Visual Disruption , 2000, Perception.

[14]  Alan Chalmers,et al.  Varying rendering fidelity by exploiting human change blindness , 2003, GRAPHITE '03.