Paper
30 April 2007 Metrics to estimate image quality in compressed video sequences
Gary O'Brien, Steven A. Israel, John M. Irvine, Charles Fenimore, John Roberts, Michelle Brennan, David Cannon, James Miller
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Abstract
A fundamental problem in image processing is finding objective metrics that parallel human perception of image quality. In this study, several metrics were examined to quantify compression algorithms in terms of perceived loss of image quality. In addition, we sought to describe the relationship of image quality as a function of bit rate. The compression schemes used were JPEG2000, MPEG2, and H.264. The frame size was fixed at 848x480 and the encoding varied from 6000 k bps to 200 k bps. The metrics examined were peak signal to noise ratio (PSNR), structural similarity (SSIM), edge localization metrics, and a blur metric. To varying degrees, the metrics displayed desirable properties, namely they were monotonic in the bit rate, the group of pictures (GOP) structure could be inferred, and they tended to agree with human perception of quality degradations. Additional work is being conducted to quantify the sensitivity of these measures with respect to our Motion Imagery Quality Scale.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gary O'Brien, Steven A. Israel, John M. Irvine, Charles Fenimore, John Roberts, Michelle Brennan, David Cannon, and James Miller "Metrics to estimate image quality in compressed video sequences", Proc. SPIE 6546, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications IV, 65460A (30 April 2007); https://doi.org/10.1117/12.718718
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Cited by 6 scholarly publications.
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KEYWORDS
Image quality

Image compression

Video

Image analysis

Image processing

Video compression

Computer programming

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