IEEE
International Conference on Image Processing, September 2002
No-Reference Perceptual
Quality Assessment of JPEG Compressed Images
Zhou
Wang, H. R. Sheikh
and Alan C. Bovik
Laboratory for Image and Video Engineering (LIVE),
Department of Electrical and Computer Engineering, The University of Texas at
Austin, Austin, TX 78712-1084
Abstract: Human observers can easily assess the
quality of a distorted image without examining the original image as a
reference. By contrast, designing objective No-Reference (NR) quality
measurement algorithms is a very difficult task. Currently, NR quality
assessment is feasible only when prior knowledge about the types of image
distortion is available.
This research aims to develop NR quality
measurement algorithms for JPEG compressed images. First, we established a JPEG
image database and subjective experiments were conducted on the database. We
show that Peak Signal-to-Noise Ratio (PSNR), which requires the reference
images, is a poor indicator of subjective quality. Therefore, tuning an NR
measurement model towards PSNR is not an appropriate approach in designing NR
quality metrics. Furthermore, we propose a computational and memory efficient
NR quality assessment model for JPEG images. Subjective test results are used
to train the model, which achieves good quality prediction performance. A
Matlab implementation of the proposed method is available at http://anchovy.ece.utexas.edu/~zwang/research/nr_jpeg_quality/index.html.