We develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of "known-item search," the retrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.

The development of objective texture similarity metrics for image analysis applications differs from that of traditional image quality metrics because substantial point-by-point deviations are possible for textures that according to human judgment are essentially identical. Thus, structural similarity metrics (SSIM) attempt to incorporate "structural" information in image comparisons. The recently proposed structural texture similarity metric (STSIM) relies entirely on local image statistics. We extend this idea further by including a broader set of local image statistics, basing the selection on metric performance as compared to subjective evaluations. We utilize both intra-and inter-subband correlations, and also incorporate information about the color composition of the textures into the similarity metrics. The performance of the proposed metrics is compared to PSNR, SSIM, and STSIM on the basis of subjective evaluations using a carefully selected set of 50 texture pairs.

No abstract

In order to facilitate the development of objective texture similarity metrics and to evaluate their performance, one needs a large texture database accurately labeled with perceived similarities between images. We propose ViSiProG, a new Visual Similarity by Progressive Grouping procedure for conducting subjective experiments that organizes a texture database into clusters of visually similar images. The grouping is based on visual blending, and greatly simplifies pairwise labeling. ViSiProG collects subjective data in an efficient and effective manner, so that a relatively large database of textures can be accommodated. Experimental results and comparisons with structural texture similarity metrics demonstrate both the effectiveness of the proposed subjective testing procedure and the performance of the metrics.Index Terms-structural similarity metrics, image quality, content-based retrieval.

Abstract-We investigate perceptual similarity metrics for the content-based retrieval of natural textures. The goal is to find perceptually similar textures that may have significant differences on a point-by-point basis. The evaluation of such metrics typically requires extensive and cumbersome subjective tests. The focus of this paper is on the recovery of textures that are "identical" to the query texture, in the sense that they are pieces of the same texture. This is important in content-based image retrieval (CBIR), where one may want to find images that contain a particular texture, as well as in some near-threshold coding applications. The advantage of evaluating metric performance in the context of retrieving identical textures is that the ground truth is known, and therefore no subjective tests are required. We can thus compare the performance of different metrics on large sets of textures, and derive meaningful statistical results. We evaluate the performance of a recently proposed structural texture similarity metric on grayscale textures, and compare it to that of PSNR, as well as space domain and complex wavelet structural similarity metrics. Experimental results with a database of 748 distinct texture images, indicate that the new metric outperforms the other metrics in the retrieval of identical textures, according to a number of standard statistical measures.