Hosik Sohn

Privacy Protection in Video Surveillance Systems: Analysis of Subband-Adaptive Scrambling in JPEG XR

This paper discusses a privacy-protected video surveillance system that makes use of JPEG extended range (JPEG XR). JPEG XR offers a low-complexity solution for the scalable coding of high-resolution images. To address privacy concerns, face regions are detected and scrambled in the transform domain, taking into account the quality and spatial scalability features of JPEG XR. Experiments were conducted to investigate the performance of our surveillance system, considering visual distortion, bit stream overhead, and security aspects. Our results demonstrate that subband-adaptive scrambling is able to conceal privacy-sensitive face regions with a feasible level of protection. In addition, our results show that subband-adaptive scrambling of face regions outperforms subband-adaptive scrambling of frames in terms of coding efficiency, except when low video bit rates are in use.

Visual comfort assessment metric based on salient object motion information in stereoscopic video

Objective assessment of visual comfort for stereoscopic video is of great importance for stereoscopic image safety issue. We propose a novel visual comfort assessment metric framework that systematically exploits human visual attention models. In a stereoscopic video shot, perceptually significant regions where human subjects pay more attention are likely to play an essential role in determining the overall level of visual comfort. As a specific example of this concept, we develop a visual comfort metric that quantifies the level of visual discomfort caused by fast salient object motion. The performance of the proposed visual comfort metric has been evaluated using natural stereoscopic videos. The experimental results show that the proposed visual comfort metric significantly improves the correlations with subjective judgment.

Full-Reference Video Quality Metric for Fully Scalable and Mobile SVC Content

Universal Multimedia Access (UMA) aims at enabling a straightforward consumption of multimedia content in heterogeneous usage environments. These usage environments may range from mobile devices in a wireless network to high-end desktop computers with wired network connectivity. Scalable video content can be used to deal with the restrictions and capabilities of diverse usage environments. However, in order to optimally tailor scalable video content along the temporal, spatial, or perceptual quality axis, a metric is needed that reliably models subjective video quality. The major contribution of this paper is the development of a novel full-reference quality metric for scalable video bit streams that are compliant with the H.264/AVC Scalable Video Coding (SVC) standard. The scalable video bit streams are intended to be used in mobile usage environments (e.g., adaptive video streaming to mobile devices). The proposed quality metric allows modeling the temporal, spatial, and perceptual quality characteristics of SVC-compliant bit streams by taking into account several properties of the compressed bit streams. These properties include the temporal and spatial variance of the video content, the frame rate, the spatial resolution, and PSNR values. An extensive number of subjective experiments have been conducted to construct and validate our quality metric. Experimental results show that the average correlation coefficient for the video sequences tested is as high as 0.95 (compared to a value of 0.60 when only using the traditional PSNR quality metric). The proposed quality metric also shows a performance that is a uniformly high for video sequences with different temporal and spatial characteristics.

Predicting Visual Discomfort of Stereoscopic Images Using Human Attention Model

We introduce a new objective assessment method for visual discomfort of stereoscopic images that makes effective use of the human visual attention model. The proposed method takes into account visual importance regions that play an important role in determining the overall degree of visual discomfort of a stereoscopic image. After obtaining a saliency-based visual importance map for an image, perceptually significant disparity features are extracted to predict the overall degree of visual discomfort. Experimental results show that the proposed method can achieve significantly higher prediction accuracy than the state-of-the-art methods.

Predicting Visual Discomfort Using Object Size and Disparity Information in Stereoscopic Images

This paper proposes object-dependent disparity features to predict the visual discomfort in stereoscopic 3-D images. The proposed object-dependent disparity features quantify the level of visual comfort influenced by disparity gradient of nearby objects and object width, respectively. They consist of relative disparity (mean of disparity difference between nearby objects) and object thickness (ratio of mean width to mean absolute disparity of an object). The prediction performance of the proposed disparity features is evaluated using various types of stereoscopic images. Experimental results demonstrate that the combined use of the proposed object-dependent disparity features substantially improve the prediction performance of the conventional disparity magnitude- and spatial complexity-related features. The performance gain ranges from 0.045 to 0.135 of correlation coefficient, compared with the feature combinations used in the conventional visual comfort metrics.

Visual Importance- and Discomfort Region-Selective Low-Pass Filtering for Reducing Visual Discomfort in Stereoscopic Displays

In human vision, the depth of focus (DOF) blur is important to perceive single binocular vision. Unfortunately, conventional stereoscopic displays cannot provide a natural DOF blur effect. Synthetic DOF blur that simulates the natural DOF blur effect could be useful for reducing the visual discomfort in stereoscopic displays. However, the incorrect introduction of artificial blur in the image might also have negative effects. This paper proposes a selective DOF blur technique that is applied only to local regions that induce high visual discomfort but are less visually important. We employ a visual importance analysis to obtain reasonable estimates of the areas/objects that are likely to be attended using saliency maps. After identifying the regions likely to cause discomfort because of excessive relative disparity, we apply low-pass filtering for the less-salient neighboring pixels of the discomfort regions. We demonstrate that the DOF blur simulation for less important background regions that induce visual discomfort is helpful in reducing the visual discomfort in stereoscopic viewing.

Attention model-based visual comfort assessment for stereoscopic depth perception

This paper investigates visual discomfort induced by depth perception for viewing stereoscopic 3D images. In order to assess the degree of visual discomfort, we exploit an attention model-based visual comfort metric, considering perceptually significant regions of stereoscopic images. In particular, the relationship between visual comfort and depth perception is modeled based on psychophysical experiments with diverse amount of binocular disparities. Then, several spatial pooling techniques are applied using a visual comfort model in order to investigate the feasibility of the attention-model based visual comfort metric. Subjective assessments have been conducted using real stereoscopic 3D images, demonstrating the efficiency of the attention model-based visual comfort metric.

Privacy Protection in Video Surveillance Systems Using Scalable Video Coding

Thanks to high-speed internet access and feature-rich mobile devices, the demand for ubiquitous and secure surveillance systems has increased. In this paper, we propose a privacy-protected video surveillance system that makes use of Scalable Video Coding (SVC). SVC can be used to fulfill the requirement of omnipresence. Further, to address privacy concerns, we detect face regions and subsequently scramble these regions-of-interest (ROIs) in the compressed domain. To demonstrate the feasibility of the proposed video surveillance system, simulation results are provided. The results show that our system is able to provide a good level of security, while offering access to surveillance video content in heterogeneous usage environments.

Visual Comfort Amelioration Technique for Stereoscopic Images: Disparity Remapping to Mitigate Global and Local Discomfort Causes

This paper proposes a new disparity remapping framework to improve the visual comfort of stereoscopic images. The proposed framework adaptively remaps disparities of a scene according to different causes of visual discomfort. A linear disparity remapping is first performed in order to address visual discomfort induced by excessive disparities. This linear remapping changes the disparities of the scene to obtain an overall target disparity range. Then, a nonlinear disparity remapping process selectively adjusts the disparity of problematic local disparity ranges according to their contribution to the visual discomfort. The proposed nonlinear disparity remapping process enables us to minimize the loss in perceived depth range while further improving visual comfort. The effectiveness of the proposed disparity remapping framework has been successfully evaluated by subjective assessments of visual comfort and naturalness. Experimental results demonstrate the validity of the proposed remapping framework. More importantly, we show that the nonlinear refinement of disparity in problematic regions can efficiently improve visual comfort while maintaining the naturalness of the scene.

Visual comfort improvement in stereoscopic 3D displays using perceptually plausible assessment metric of visual comfort

Visual discomfort is caused by several factors such as accommodation-vergence conflict and window violation in conventional stereoscopic displays. To ensure comfortable conditions in the stereoscopic viewing, it is essential to develop an effective way of improving visual comfort in stereoscopic displays. This paper proposes a new method for visual comfort improvement that adjusts the disparity of a scene under the guidance of an objective assessment metric of visual comfort. The proposed method employs a visual comfort assessment metric that takes into account various discomfort factors in the stereoscopic viewing. The results of subjective assessment experiments demonstrate that the proposed method can efficiently improve visual comfort in the stereoscopic viewing.