TY - GEN
T1 - Subjective and objective texture similarity for image compression
AU - Zujovic, Jana
AU - Pappas, Thrasyvoulos N.
AU - Neuhoff, David L.
AU - Van Egmond, Rene
AU - De Ridder, Huib
PY - 2012
Y1 - 2012
N2 - We focus on the evaluation of texture similarity metrics for structurally lossless or nearly structurally lossless image compression. By structurally lossless we mean that the original and compressed images, while they may have visible differences in a side-by-side comparison, they have similar quality so that one cannot tell which is the original. This is particularly important for textured regions, which can have significant point-by-point differences, even though to the human eye they appear to be the same. As in traditional metrics, texture similarity metrics are expected to provide a monotonic relationship between measured and perceived distortion. To evaluate metric performance according to this criterion, we introduce a systematic approach for generating synthetic texture distortions that model variations that occur in natural textures. Based on such distortions, we conducted subjective experiments with a variety of original texture images and different types and degrees of distortions. Our results indicate that recently proposed structural texture similarity metrics provide the best performance.
AB - We focus on the evaluation of texture similarity metrics for structurally lossless or nearly structurally lossless image compression. By structurally lossless we mean that the original and compressed images, while they may have visible differences in a side-by-side comparison, they have similar quality so that one cannot tell which is the original. This is particularly important for textured regions, which can have significant point-by-point differences, even though to the human eye they appear to be the same. As in traditional metrics, texture similarity metrics are expected to provide a monotonic relationship between measured and perceived distortion. To evaluate metric performance according to this criterion, we introduce a systematic approach for generating synthetic texture distortions that model variations that occur in natural textures. Based on such distortions, we conducted subjective experiments with a variety of original texture images and different types and degrees of distortions. Our results indicate that recently proposed structural texture similarity metrics provide the best performance.
KW - image quality
KW - perceptual similarity
UR - http://www.scopus.com/inward/record.url?scp=84867609792&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867609792&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2012.6288145
DO - 10.1109/ICASSP.2012.6288145
M3 - Conference contribution
AN - SCOPUS:84867609792
SN - 9781467300469
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1369
EP - 1372
BT - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
T2 - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Y2 - 25 March 2012 through 30 March 2012
ER -