Quality Assessment for DIBR-Synthesized Views Based on Wavelet Transform and Gradient Magnitude Similarity

To drive upgrades of Depth-Image-Based Rendering (DIBR) algorithms, depth image refinement, etc., quality assessment models for DIBR-synthesized images in 3D video systems are developed. However, most of these models could not effectively evaluate distortion due to irregular stretching (e.g., crumbling), which is more complex and common than black holes and regular stretching (e.g., horizontal stretching) in synthesized images. To make an attempt at this issue, a new quality assessment method is proposed for DIBR views. First, feature point matching and affine transformation are adopted to remove and compensate for the global object shift between reference and synthesized view images. Second, multi-scale discrete wavelet transform is utilized to extract multi-scale structure distortion; gradient magnitude similarity is further integrated to highlight the distortion features; morphological open operation and median filtering are adopted to exclude perceptually unimportant features. Third, scores are obtained by standard deviation pooling on distortion feature maps for each wavelet scale and sub-band. Experimental results demonstrate that our proposed model outperforms the state-of-the-art handcrafted feature-based DIBR-synthesized image quality assessment models on IETR database, and performs the best on average on IETR and IRCCyN/IVC databases. The source code will be available at https://github.com/House-yuyu/DIBR_IQA .