Assessing Objective Image Quality Metrics for Bidirectional Texture Functions

Bidirectional Texture Functions (BTFs) are view- and illumination-dependent textures used in rendering for accurate simulation of the complex reflectance behavior of fabrics. One major issue in BTF rendering is the large number and size of images which requires lots of storage. "Visually lossless" compression offers the potential to use higher compression levels without noticeable artifacts, but requires a rate-control strategy that adapts to image content and loss visibility. In this contribution, we investigate the applicability of objective image quality metrics to predict levels of perception degradation for compressed BTF textures. We apply traditional error-sensitivity and structural similarity based approaches to predict levels of perceptibility for compressed BTF textures to achieve visually lossless compression. To confirm the validity of the present study, the results of an experimental study on how decreasing the BTF texture resolution influences the perceived quality of the rendered images with the results of the applied image quality metrics are compared. In order to compare two representatives from each group were selected. The Visible Differences Predictor (VDP) and Visual Discrimination Model (VDM) are typical examples of an image quality metric based on error sensitivity, whereas the Structural SIMilarity index (SSIM) and Complex Wavelet Domain Structural Similarity Index (CWSSIM) are specific examples of a structural similarity quality measure.