Abstract
Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, many DIBR-synthesized image quality assessment (IQA) algorithms have been proposed to quantify the distortion that existed in texture images. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to accurately evaluate the visual quality of DIBR-synthesized images. To this end, this paper presents a DIBR-synthesized image quality assessment metric with Texture and Depth Information, dubbed as TDI. TDI predicts the quality of DIBR-synthesized images by jointly measuring the synthesized image's colorfulness, texture structure, and depth structure. The design principle of our TDI includes two points: (1) DIBR technologies bring color deviation to DIBR-synthesized images, and so measuring colorfulness can effectively predict the quality of DIBR-synthesized images. (2) In the hole-filling process, DIBR technologies introduce the local geometric distortion, which destroys the texture structure of DIBR-synthesized images and affects the relationship between the foreground and background of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality through a joint representation of texture and depth structures. Experiments show that our TDI outperforms the competing state-of-the-art algorithms in predicting the visual quality of DIBR-synthesized images.
Highlights
With the advent of the 5G era and the advancement of 3-dimensional display technology, video technology moves from “seeing clearly” to the ultra-high definition and immersive virtual reality era of “seeing the reality.” Free-viewpoint videos (FVVs) have broad applications in entertainment, education, medical treatment, military applications for its ability to provide users with visual information of integrity, immersion, and interactivity (Selzer et al, 2019; Yildirim, 2019)
These methods ignore the influence of color deviation distortion on the visual quality of DIBR-synthesized images. These methods only focus on estimating the geometric distortion and blur distortion from textured images without considering the local geometric distortion’s adverse effects on the synthesized image’s depth structure
We construct experiments on the IRCCyN/IVC database to test the performance of the proposed TDI method and other SOTA image quality assessment (IQA) algorithms
Summary
With the advent of the 5G era and the advancement of 3-dimensional display technology, video technology moves from “seeing clearly” to the ultra-high definition and immersive virtual reality era of “seeing the reality.” Free-viewpoint videos (FVVs) have broad applications in entertainment, education, medical treatment, military applications for its ability to provide users with visual information of integrity, immersion, and interactivity (Selzer et al, 2019; Yildirim, 2019). Researchers have realized that IQA algorithms for natural images have difficulty in estimating the geometric distortion prevalent in DIBR-synthesized images For this problem, Bosc et al (2011) calculated the difference map between the synthesized image and the reference image based on SSIM and adopted a threshold strategy to detect the disoccluded area in the synthesized image. Based on the local geometric distortion measurement, Yue et al (2019)’s and Li et al (2018b)’s methods introduce global sharpness estimation to predict the synthesized image quality. These methods ignore the influence of color deviation distortion on the visual quality of DIBR-synthesized images These methods only focus on estimating the geometric distortion and blur distortion from textured images without considering the local geometric distortion’s adverse effects on the synthesized image’s depth structure.
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