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Image Quality Assessment for Multi-view 3D CG Images and 5K High Definition Images Based on S-CIELAB Color Space
Abstract
In this paper, we assessed subjective quality of 3D CG images by H.265/HEVC with both multi-view parallax barrier and 5K high-definition retina, and then, we analyzed them, and classified by Support Vector Machine. Next, we assessed objective quality by measuring luminance by S-CIELAB color space, and color difference by CIEDE2000.
... On the other hand, since even if 4K broadcasting is started, all broadcasting video contents are not in 4K video quality, it is difficult to represent texture or Shitsukan of images originally. Therefore, for an example, HDTV quality video is transformed to 4K quality by processing of superresolution [1], and then, we will need to reproduce texture of 4K quality as possible in the near future. Up to now, in "Shitsukan (in Japanese)", there are a lot of meaning and interpretation. ...
... As experimental procedure, we show (1)-(4) in the following. (1). There are 10,355 images in the Shitsukan Research Database [8]. ...
In this paper, we used the Shitsukan Research Database from Web for free of charge. First, we generated the texture evaluation images by H.265/HEVC. And then, we assessed the generated images by texture analysis, and discussed results. Next, based on experimental results, we considered for classification method of texture types by Support Vector Machine (SVM). Finally, we discussed including assumption of system's automation.
Previously, it is not obvious to what extent was accepted for the assessors when we see the 3D image (including multi-view 3D) which the luminance change may affect the stereoscopic effect and assessment generally. We think that we can conduct a general evaluation, along with a subjective evaluation, of the luminance component using both the S-CIELAB color space and CIEDE2000. In this study, first, we performed three types of subjective evaluation experiments for contrast enhancement in an image by using the eight viewpoints parallax barrier method. Next, we analyzed the results statistically by using a support vector machine (SVM). Further, we objectively evaluated the luminance value measurement by using CIEDE2000 in the S-CIELAB color space. Then, we checked whether the objective evaluation value was related to the subjective evaluation value. From results, we were able to see the characteristic relationship between subjective assessment and objective assessment.
Recently, the use of 3D video systems without glasses has increased, and therefore 3D image quality and presence evaluation is important. There are various stereo-logical image quality evaluation methods for multi-view 3D systems without glasses. However, there is no uniform method for evaluating 3D video systems. In this study, we focus on camera interval and JPEG coding degradation with a multi-view 3D system. Previously, many studies have examined camera interval or JPEG coding degradation with 3D glasses or the binocular method. In such systems, viewers perceive stereoscopic and depth effects. Moreover, they can see from different angles, increasing viewpoints with multi-view 3D systems. However, viewers feel discomfort when changing their viewpoint. Hence, we consider, in particular, the accommodation of the camera interval and JPEG coding degradation while changing viewpoints. We have performed subjective evaluations using the absolute category rating system to assess the effects of changing the camera interval of 3D CG images or video content using an 8 viewpoint lenticular lens method. We measure assessors' ability to identify the degree of the camera interval. We analyze the results of our subjective evaluations statistically and discuss the results. Using the optimal camera interval, we perform a subjective quality evaluation employing the double stimulus impairment scale to determine assessors' ability to identify JPEG coding degradation by degree. The experimental results of this subjective evaluation are also statistically analyzed.
Many previous studies on image quality assessment of 3D still images or video clips have been conducted. In particular, it is important to know the region in which assessors are interested or on which they focus in images or video clips, as represented by the ROI (Region of Interest). For multi-view 3D images, it is obvious that there are a number of viewpoints; however, it is not clear whether assessors focus on objects or background regions. It is also not clear on what assessors focus depending on whether the background region is colored or gray scale. Furthermore, while case studies on coded degradation in 2D or binocular stereoscopic videos have been conducted, no such case studies on multi-view 3D videos exist, and therefore, no results are available for coded degradation according to the object or background region in multi-view 3D images. In addition, in the case where the background region is gray scale or not, it was not revealed that there were affection for gaze point environment of assessors and subjective image quality. In this study, we conducted experiments on the subjective evaluation of the assessor in the case of coded degradation by JPEG coding of the background or object or both in 3D CG images using an eight viewpoint parallax barrier method. Then, we analyzed the results statistically and classified the evaluation scores using an SVM.
Recent work in color difference has led to the recommendation of CIEDE2000 for use as an industrial color difference equation. While CIEDE2000 was designed for predicting the visual difference for large isolated patches, it is often desired to determine the perceived difference of color images. The CIE TC8-02 has been formed to examine these differences. This paper presents an overview of spatial filtering combined with CIEDE2000, to assist TC8-02 in the evaluation and implementation of an image color difference metric. Based on the S-CIELAB spatial extension, the objective is to provide a single reference for researchers desiring to utilize this technique. A general overview of how S-CIELAB functions, as well as a comparison between spatial domain and frequency domain filtering is provided. A reference comparison between three CIE recommended color difference formulae is also provided. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 425–435, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10195
10: Result of Exp. 3-1 (∆í µí±¬í µí±¬ í µí¿í µí¿í µí¿í µí¿
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Fig. 10: Result of Exp. 3-1 (∆í µí±¬í µí±¬ í µí¿í µí¿í µí¿í µí¿, Multi-view)
11: Result of Exp. 3-2 (∆í µí±¬í µí±¬ í µí¿í µí¿í µí¿í µí¿ , from Superresolution to HEVC)
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Fig. 11: Result of Exp. 3-2 (∆í µí±¬í µí±¬ í µí¿í µí¿í µí¿í µí¿, from Superresolution to HEVC)
12: Result of Exp. 3-3 (∆í µí±¬í µí±¬ í µí¿í µí¿í µí¿í µí¿ , from HEVC to Super-resolution)
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Fig. 12: Result of Exp. 3-3 (∆í µí±¬í µí±¬ í µí¿í µí¿í µí¿í µí¿, from HEVC to
Super-resolution)
11: Result of Exp. 3-2 ( , from Superresolution to HEVC)
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Fig. 11: Result of Exp. 3-2 (, from Superresolution to HEVC)
12: Result of Exp. 3-3 ( , from HEVC to Super-resolution)
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Fig. 12: Result of Exp. 3-3 (, from HEVC to
Super-resolution)