Measuring Coarse-to-Fine Texture and Geometric Distortions for Quality Assessment of DIBR-Synthesized Images
IEEE Transactions on Multimedia(2021)
摘要
A synthesized view can be generated via Depth-Image-Based Rendering (DIBR) technique using one (or more) color images and the associated depth maps. However, several artifacts may occur in the synthesized views due to the imperfect color images, depth maps or texture inpainting techniques, which cannot be effectively estimated by the conventional quality metrics designed for natural images. In this paper, a new quality metric is proposed to evaluate DIBR-synthesized images by measuring texture and geometric distortions. The artifacts are first analyzed on different phases of the synthesis process, and the associated features are extracted to estimate the degree of texture and geometric distortions from both coarse and fine scales. Finally, individual quality scores are aggregated into an overall quality via regression. Experimental results on three publicly available DIBR datasets demonstrate the superiority of the proposed method over the state-of-the-art quality models.
更多查看译文
关键词
Quality assessment,view synthesis,DIBR,texture distortion,geometric distortion,coarse-to-fine
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络