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.

Image quality evaluation is very important. In applications involving signal transmission, the Reduced- or No-Reference quality metrics are generally more practical than the Full-Reference metrics. In this study, we propose a quality estimation method based on a novel semi-fragile and adaptive watermarking scheme. The proposed scheme uses the embedded watermark to estimate the degradation of cover image under different distortions. The watermarking process is implemented in DWT domain of the cover image. The correlated DWT coefficients across the DWT subbands are categorized into Set Partitioning in Hierarchical Trees (SPIHT). Those SPHIT trees are further decomposed into a set of bitplanes. The watermark is embedded into the selected bitplanes of the selected DWT coefficients of the selected tree without causing significant fidelity loss to the cover image. The accuracy of the quality estimation is made to approach that of Full-Reference metrics by referring to an "Ideal Mapping Curve" computed a priori. The experimental results show that the proposed scheme can estimate image quality in terms of PSNR, wPSNR, JND and SSIM with high accuracy under JPEG compression, JPEG2000 compression, Gaussian low-pass filtering and Gaussian noise distortion. The results also show that the proposed scheme has good computational efficiency for practical applications.

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.

High Definition (HD) content delivery over IP networks is now a reality in the entertainment marketplace. Such networks can suffer packet loss, especially on the last mile link. This results in time-variant video quality. In this paper we first present an analysis of the human perception of network impairment on HD IPTV quality. Second, we propose a method, based on common objective measurements, to estimate subjective judgments of time-variant quality. A testbed was deployed to emulate a real use case of delivering high definition TV material over an IPTV network. The quality of the delivered H.264/AVC encoded video was evaluated objectively in an emulated environment where packet loss impairments were generated. Objective measurements were then compared to continuous subjective quality ratings. Based on the obtained results, highly correlated models were developed for the estimation of instantaneous user judgment of impaired HD broadcast IPTV.

In the stereoscopic high-definition (HD) frame-compatible formats, the separate left and right views are reduced in resolution and packed to fit within the same video frame as a conventional two-dimensional high-definition signal. Since they do not require additional transmission bandwidth and entail limited changes to the existing broadcasting infrastructure, these formats have been suggested for 3DTV. However, the convenience of frame-compatible formats comes at the expense of lower picture quality of the 3D signal. In this study, we evaluated the loss in picture quality of two frame-compatible formats: 1080i Side-by-Side and 720p Top/Bottom, using a subjective assessment experiment.

In the last few years, Internet Protocol Television (IPTV) has emerged as one of the major distribution technologies for broadband multimedia services. However, the delivery of High Definition Television (HDTV) services over IP networks still faces many challenges. This paper presents a study on quality assessment of HD video content for IPTV applications. Video sequences with different content types are delivered through an IPTV testbed. Subject to packet loss, their quality is evaluated with common objective video quality measurement tools in the emulated error-prone environment with respect to their content complexity. Objective criticality based on the calculation of spatio-temporal activities is examined to validate its suitability as video content complexity indicator in quality assessment. Subjective assessment is further carried out to validate the objective test results. With a good correlation between subjective Mean Opinion Score (MOS) and objective metrics, the prediction of viewer satisfaction in terms of HD video content quality in IPTV networks becomes feasible. Suggestions are also made regarding the choice of suitable measurements for more meaningful HD content classifications.

A framework for virtual view synthesis based on multiple images is presented in this paper. Compared to conventional view synthesis based on stereoscopic image pairs, a postprocessing algorithm for disparity refinement is added to exploit information contained in multiple images captured with a multi-view camera configuration. The principle for disparity refinement is examined, leading to the development of a novel algorithm. Experimental results show that the newly developed algorithm can improve image quality of synthesized virtual views with a PSNR gain of up to 0.65 dB.

As a practical and novel application of watermarking, this paper presents a digital watermarking-based image quality evaluation method that can accurately estimate image quality in terms of the classical objective metrics, such as peak signal-to-noise ratio (PSNR), weighted PSNR (wPSNR), and Watson just noticeable difference (JND), without the need for the original image. In this method, a watermark is embedded into the discrete wavelet transform (DWT) domain of the original image using a quantization method. Considering that different images have different frequency distributions, the vulnerability of the watermark for the image is adjusted using automatic control. After the auto-adjustment, the degradation of the extracted watermark can be used to estimate image quality in terms of the classical metrics with high accuracy. We calculated PSNR, wPSNR, and Watson JND quality measures for JPEG compressed images and compared the values with those estimated using the watermarking-based approach. We found that the calculated and estimated measures of quality to be highly correlated, suggesting that the proposed method can provide accurate measures of image quality under JPEG compression. Furthermore, given the similarity between JPEG and MPEG-2, this achievement has paved the road for the practical and accurate quality evaluation of MPEG-2 compressed video. We believe that this achievement is of great importance to video broadcasting

The conventional two-dimensional wavelet transform used in existing image coders is usually performed through one-dimensional (1-D) filtering in the vertical and horizontal directions, which cannot efficiently represent edges and lines in images. The curved wavelet transform presented in this paper is carried out by applying 1-D filters along curves, rather than being restricted to vertical and horizontal straight lines. The curves are determined based on image content and are usually parallel to edges and lines in the image to be coded. The pixels along these curves can be well represented by a small number of wavelet coefficients. The curved wavelet transform is used to construct a new image coder. The code-stream syntax of the new coder is the same as that of JPEG2000, except that a new marker segment is added to the tile headers. Results of image coding and subjective quality assessment show that the new image coder performs better than, or as well as, JPEG2000. It is particularly efficient for images that contain sharp edges and can provide a PSNR gain of up to 1.67 dB for natural images compared with JPEG2000.

For the purpose of temporally scalable video coding and multi-dimensional bit rate control, we conducted experiments of subjective quality assessment for video sequences encoding with variable frame rates and quantization parameters. The experimental results show that the correlation between the PSNR and the subjective quality becomes very low when the two coding parameters vary simultaneously. Then, we propose a new quality metric that takes into account the quantization errors, frame rates, and motion speeds. This metric has a higher correlation with the subjective quality. The average correlation coefficient for five video sequences is as high as 0.93, whereas that of the PSNR is only 0.70.