Metrics to Estimate Image Quality in Compressed Video Sequences

Published

April 30, 2007

Author(s)

G O'Brien, S Israel, J Irvine, Charles D. Fenimore, John W. Roberts, Michelle Brennan, D Cannon, J Miller

Abstract

A fundamental problem in image processing is to find 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 in 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.

Proceedings Title

Proceedings SPIE Security and Defense Symposium

Conference Dates

April 9-13, 2007

Conference Location

Orlando, FL, USA

Conference Title

SPIE Security and Defense Symposium

Pub Type

Conferences

Keywords

NIIRS, image quality, metrics, motion imagery, compression

Citation

O'Brien, G. , Israel, S. , Irvine, J. , Fenimore, C. , Roberts, J. , Brennan, M. , Cannon, D. and Miller, J. (2007), Metrics to Estimate Image Quality in Compressed Video Sequences, Proceedings SPIE Security and Defense Symposium, Orlando, FL, USA (Accessed October 13, 2024)