Multi-view 3D CG Image Quality Assessment for Contrast Enhancement Including S-CIELAB Color Space in case the Background Region is Gray Scale

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In this paper, we experimented the subjective evaluation for 3D CG image including the gray scale region with 8 viewpoints parallax barrier method, and we analyzed this result statistically. Next, we measured about the relation between the luminance change and the color difference by using S-CIELAB color space and CIEDE2000. As a result, we obtained knowledge about the relation among the coded image quality, the contrast enhancement, and gray scale.

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... Fig. 4 is shown from loading original image and evaluation image, transforming to S-CIELAB color space and calculating to CIEDE2000. In detail, you would like to refer to [13], [14], and [15]. ...
... This tendency is represented from Exp. 2 and 3, however, in Exp. 1, we estimate the relation to color information. In 3D CG images used in Author's references [5], [14], [15], there are change for luminance and color difference by contrast enhancement and image resolution. Therefore, we estimate applying this knowledge in laparoscopic image in this study. ...
In this paper, first, we generated medical images cut as frame still image from laparoscopic video acquired by endoscopy. Using these images, we processed to encode and decode by H.265/HEVC in certain image regions, and we generated evaluation images. Next, we evaluated objectively seeing from the coded image quality by using PSNR (Peak Signal to Noise Ratio), considering the automatic detection of coded defect region information. Furthermore, we analyzed for color information by measuring both the luminance using S-CIELAB color space and the color difference using CIEDE2000. Finally, we try to classify effectively using Support Vector Machine (SVM), and we discussed including the automatic detection of coded defect region information whether it is possible for application of medical image diagnosis or not.
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