Texture Smoothing Quality Assessment via Information Entropy

Image texture smoothing (ITS) aims at completely removing textures while preserving as much as possible different-scale structures of an image. However, few quality assessment metrics have been formulated to objectively evaluate the ITS results, due to the lack of ground-truth texture-free images. Considering both the smoothness of textures and the preservation of structures, we make the debut of objective evaluation of ITS results, and design an intuitive Texture Smoothness and Structural Similarity Index (T3SI) based on human perception. Specifically, we first intuitively select some patches which contain relatively more texture/structure information. We then employ the Edge Preservation Index and the Structural Similarity Index Measure to evaluate the texture smoothness and the structure similarity on the selected patches, respectively. We finally formulate the novel exponential entropy function to balance the texture smoothness and the structure similarity for the quality assessment of ITS. T3SI is objective, easy to use, simple to implement, and stable. An independent User Study is performed to verify our proposed T3SI, and large experiments show that T3SI competes successfully to the state-of-the-art metrics. Our code is publicly available.