Visual discomfort induced by inharmonious human 3D perceptions between the real world and a display is encountered most commonly in stereoscopic displays. Although various state-of-the-art 3D technologies have reduced the gaps, these delicate differences yield eye fatigue during adaptation of the human brain to artificial 3D information in displays. Therefore, the assessment of visual discomfort, either in producing or reproducing 3D content, is essential to improve viewers' quality of experience. In this paper, we used EEG to investigate the feasibility of visual discomfort assessment on stereo images. To find features of brain-waves representing 3D visual discomfort, we designed 3D stereo image experiments and collected EEG data for several subjects. We found that spectral attenuation of alpha and beta bands over the sensorimotor area and temporal features detected 2 to 4 seconds after onset could be strong indicators of visual discomfort.Index Terms-Visual discomfort, eye fatigue, EEG, stereoscopic, quality of experience.

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Three-dimensional (3D) shape reconstruction from one or multiple observations is a primary problem of computer vision. Shape from Focus (SFF) is a passive optical method that uses multiple twodimensional (2D) images with different focus levels. When obtaining 2D images in each step along the optical axis, mechanical vibrations, referred as jitter noise, occur. SFF techniques are vulnerable to jitter noise that can vary focus values in 2D images. In this paper, new filtering method, which provides high accuracy of 3D shape reconstruction and low computational cost, is proposed. First, jitter noise is modeled as Lévy distribution. This assumption makes it possible to show the influence of proposed filtering method in real environment with non-Gaussian jitter noise. Second, focus curves are modeled as Gaussian function to compare the performance of proposed filtering method with those of the conventional filtering methods. Finally, improved maximum correntropy criterion Kalman filter (IMCC-KF) is designed as a post-processing step, and is applied to the modeled focus curves. The experiments are performed on real and synthetic objects and the results demonstrate the effectiveness of proposed method. INDEX TERMS Focus curve, improved maximum correntropy criterion Kalman filter (IMCC-KF), jitter noise, non-Gaussian distribution, shape from focus (SFF).

We study optical technologies for viewer-tracked autostereoscopic 3D display (VTA3D), which provides improved 3D image quality and extended viewing range. In particular, we utilize a technique-the so-called dynamic fusion of viewing zone (DFVZ)-for each 3D optical line to realize image quality equivalent to that achievable at optimal viewing distance, even when a viewer is moving in a depth direction. In addition, we examine quantitative properties of viewing zones provided by the VTA3D system that adopted DFVZ, revealing that the optimal viewing zone can be formed at viewer position. Last, we show that the comfort zone is extended due to DFVZ. This is demonstrated by a viewer's subjective evaluation of the 3D display system that employs both multiview autostereoscopic 3D display and DFVZ.

Background/Aims: In exploring human factors, stereoscopic 3D images have been used to investigate the neural responses associated with excessive depth, texture complexity, and other factors. However, the cortical oscillation associated with the complexity of stereoscopic images has been studied rarely. Here, we demonstrated that the oscillatory responses to three differently shaped 3D images (circle, star, and bat) increase as the complexity of the image increases. Methods: We recorded simultaneous EEG/MEG for three different stimuli. Spatio-temporal and spatio-spectro-temporal features were investigated by non-parametric permutation test. Results: The results showed that N300 and alpha inhibition increased in the ventral area as the shape complexity of the stereoscopic image increased. Conclusion: It seems that the relative disparity in complex stereoscopic images may increase cognitive processing (N300) and cortical load (alpha inhibition) in the ventral area.

In the 3D video system including depth information, once a depth video is coded by the state-of-the-art video compression tools such as H.264/AVC, depth errors around the boundaries of objects can be intensified, and these can significantly affect the quality of rendered virtual view later. Despite this drawback of depth video coding, its compression is essential because of the enormous amount of input data in the 3D video system. In this paper, we propose a line-based partitioned intra prediction method which exploits geometric redundancy of depth video for an efficient compression without significant errors around boundaries. The proposed algorithm can efficiently divide the current coded block into two partitioned regions, and the algorithm independently predicts each region with previously coded neighboring pixels. Finally, the generated prediction mode adaptively alternates the conventional DC intra prediction mode. To evaluate the intra prediction performances, we have implemented the proposed method into H.264/AVC intra prediction scheme. Experimental results have demonstrated that our proposed method provides higher coding performance. The coding performance for depth video compression itself was up to 4.43% bit-saving or 0.526 dB gain in terms of peak signal-to-noise ratio (PSNR) among proper depth sequences which contain line-like boundaries.

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