Metrics to estimate image quality in compressed video sequences
2007
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.
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