Local structure divergence index for image quality assessment

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7667 LNCS (PART 5), pp. 337 - 344
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012004182OK.pdf873.29 kB
Adobe PDF
Image quality assessment (IQA) algorithms are important for image-processing systems. And structure information plays a significant role in the development of IQA metrics. In contrast to existing structure driven IQA algorithms that measure the structure information using the normalized image or gradient amplitudes, we present a new Local Structure Divergence (LSD) index based on the local structures contained in an image. In particular, we exploit the steering kernels to describe local structures. Afterward, we estimate the quality of a given image by calculating the symmetric Kullback-Leibler divergence (SKLD) between kernels of the reference image and the distorted image. Experimental results on the LIVE database II show that LSD performs consistently with the human perception with a high confidence, and outperforms representative structure driven IQA metrics across various distortions. © 2012 Springer-Verlag.
Please use this identifier to cite or link to this item: