Paper
7 June 2004 Automatic attention-based prioritization of unconstrained video for compression
Author Affiliations +
Proceedings Volume 5292, Human Vision and Electronic Imaging IX; (2004) https://doi.org/10.1117/12.527057
Event: Electronic Imaging 2004, 2004, San Jose, California, United States
Abstract
We apply a biologically-motivated algorithm that selects visually-salient regions of interest in video streams to multiply-foveated video compression. Regions of high encoding priority are selected based on nonlinear integration of low-level visual cues, mimicking processing in primate occipital and posterior parietal cortex. A dynamic foveation filter then blurs (foveates) every frame, increasingly with distance from high-priority regions. Two variants of the model (one with continuously-variable blur proportional to saliency at every pixel, and the other with blur proportional to distance from three independent foveation centers) are validated against eye fixations from 4-6 human observers on 50 video clips (synthetic stimuli, video games, outdoors day and night home video, television newscast, sports, talk-shows, etc). Significant overlap is found between human and algorithmic foveations on every clip with one variant, and on 48 out of 50 clips with the other. Substantial compressed file size reductions by a factor 0.5 on average are obtained for foveated compared to unfoveated clips. These results suggest a general-purpose usefulness of the algorithm in improving compression ratios of unconstrained video.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Laurent Itti "Automatic attention-based prioritization of unconstrained video for compression", Proc. SPIE 5292, Human Vision and Electronic Imaging IX, (7 June 2004); https://doi.org/10.1117/12.527057
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Cited by 12 scholarly publications.
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KEYWORDS
Video

Video compression

Eye

Computer programming

Eye models

Visualization

Calibration

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