No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users’ visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images.

Optimising the illumination spectrum for enhancing tissue visualisation

In operations, light reflected from biological tissue can be used for disease detection. In this paper, we used a visual entropy evaluation method to design the optimal illuminant to improve colour discriminability of biological tissue. The optimal spectral power distribution of the illuminant was obtained by maximising the visual entropy value of sample tissue based on the human visual system. In the experiment, multispectral imaging was used to measure the spectral reflectance of the tissue and colour clustering was conducted to extract its colour features. To verify the effectiveness of this method, simulated tissue images under illuminations with optimised spectral power distributions were compared with those under other light sources such as the standard illuminant D65 and white LED light sources. Results show that the sample under optimised illumination has a higher visual entropy value with better perceptual visibility.