Abstract
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.
Highlights
Texture, as a set of texture elements or texels occurring in some regular or repetitive patterns, can be artificially created in an image or found in the captured natural scene images, such as mosaics, sand, and rocks
The pseudo code of our T3SI is detailed in Algorithm 1 Texture IQA Index T3SI Input: Original image I0 and filtered image I1 Output: T3SI 1: pi, qi ← Manual positions selected in I0 2: r ← Set a patch radius 3: S, S ← Select patches centered at pi, qi from I0 4: S1, S1 ← Select patches centered at pi, qi from I1 5: 1 − Edge Preservation Index (EPI) ← Computed by Eq (1) according to S, S 6: Structure Similarity Index Measure (SSIM) ← Computed by Eq (2)according to S1, S1 7: ξ, θ ← Normalize 1 − EPI and SSIM by Eq (4) - (5) 8: T 3SI ← Obtain the evaluation result through Eq (6)
We present an interactive assessment method T3SI for measuring texture filtered images that are difficult to evaluate by existing IQA metrics
Summary
As a set of texture elements or texels occurring in some regular or repetitive patterns, can be artificially created in an image or found in the captured natural scene images, such as mosaics, sand, and rocks. Texture filtering should focus on the texture smoothness rather than the sharpness of the texture These quality evaluation methods based on local details only analyze the edge features of the image while not synthesizing the texture smoothness and the structural retention, which could not achieve a comprehensive quality measurement. To fully compare the overall performance on the measurement of texture filtered images, the early common methods: Mean Square Error (MSE) [9], Peak-Signal to Noise Ratio (PSNR) [10] and Structure Similarity Index Measure (SSIM) [11] have been utilized for comprehensively evaluating filtered images by some texture filters [12]–[14]. In this work, aiming at the subjective definition of textures, the locality of texture details, and the comprehensiveness of IQA, we integrate texture smoothness and structure similarity to construct a comprehensive objective evaluation index T3SI, based on the intuitive selection of textures and features.
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