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Abstract: The proposed interpolation filter comprises two concatenating filters, adaptive pre-interpolation filter (APIF) and the normative interpolation filter in H.264/AVC. The former is applied only to the integer pixels in the reference frames; the latter generates all the sub-position samples, supported by the output of APIF. The convolution of APIF and the standard filter minimizes the motion prediction error on a frame basis. APIF preserves the merits of the adaptive interpolation filter (AIF) and the adaptive loop filter (ALF) in the key technical area (KTA) software and at the same time overcomes their drawbacks. The experimental results show that APIF outperforms either AIF or ALF. Compared with the joint use of AIF and ALF, APIF provides comparable performance, but has much lower complexity. [Copyright &y& Elsevier]

Abstract: In this paper, we present an automatic algorithm to segment multiple objects from multi-view video. The Initial Interested Objects (IIOs) are automatically extracted in the key view of the initial frame based on the saliency model. Multiple objects segmentation is decomposed into several sub-segmentation problems, and solved by minimizing the energy function using binary label graph cut. In the proposed novel energy function, the color and depth cues are integrated with the data term, which is then modified with background penalty with occlusion reasoning. In the smoothness term, foreground contrast enhancement is developed to strengthen the moving objects boundary, and at the same time attenuates the background contrast. To segment the multi-view video, the coarse predictions of the other views and the successive frame are projected by pixel-based disparity and motion compensation, respectively, which exploits the inherent spatiotemporal consistency. Uncertain band along the object boundary is shaped based on activity measure and refined with graph cut, resulting in a more accurate Interested Objects (IOs) layer across all views of the frames. The experiments are implemented on a couple of multi-view videos with real and complex scenes. Excellent subjective results have shown the robustness and efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an automatic human body segmentation system which mainly consists of human body detection and object segmentation. Firstly, an automatic human body detector is designed to provide hard constraints on the object and background for segmentation. And a coarse-to-fine segmentation strategy is employed to deal with the situation of partly detected object. Secondly, background contrast removal (BCR) and self-adaptive initialization level set (SAILS) are proposed to solve the tough segmentation problems of the high contrast at object boundary and/or similar colors existing in the object and background. Finally, an object updating scheme is proposed to detect and segment new object when it appears in the scene. Experimental results demonstrate that our body segmentation system works very well in the live video and standard sequences with complex background. [Copyright &y& Elsevier]

Abstract: Compressed video bit stream is very sensitive to transmission errors, due to error propagation resulting from variable-length coding, differential coding, and motion compensated prediction. There exist many error-resilient techniques that can significantly improve the reconstructed video quality by reducing the spatial and temporal error propagation. In this paper, an error-resilient coding scheme that can limit the spatial error propagation with little overhead is proposed. It disables intra AC prediction and groups appropriate fixed length coded (FLC) syntaxes in a video packet (or slice) together to form a new partition. With intra AC prediction disabled, errors occurred in these FLC syntaxes will not cause spatial error propagation. This error resilient coding scheme essentially classifies the syntaxes into two categories according to whether that syntax will cause spatial error propagation when an error occurs. We evaluate the efficiency of the error-resilient coding scheme by comparing the reconstructed PSNR of the proposed coding scheme with that of the MPEG-4 coding scheme over the AWGN channel and the Rayleigh channel. Depending on the video sequence, channel coding rate and channel condition, our proposed data-partitioning scheme can achieve up to 1.8dB PSNR gain. [Copyright &y& Elsevier]

Abstract: In this paper, we study joint source channel coding for bitplane based video coding over wireless channels. We consider using frame-level intra-mode to stop error propagation and using unequal error protection (UEP) to combat channel errors. Our focus is on how to optimally select coding modes and find UEP solutions for bitplane based video coding. In particular, we propose an overall end-to-end rate–distortion (R–D) function, which considers not only the source distortion and the channel distortion introduced in the current frame but also the propagated channel distortion from the previous frames. Based on this end-to-end R–D function, we are able to find the optimal solutions for both mode selection and UEP so that an optimal tradeoff between efficiency and robustness can be achieved. Experimental results demonstrate the significant performance gain. [Copyright &y& Elsevier]

A multi-layer authentication scheme for HEVC compressed video is proposed. The combination of CU sizes, which is unique to HEVC and sensitive to video manipulation, is considered along with other elements in the HEVC coding standard to generate the authentication tag. Temporal dependency was enforced, where the authentication tag generated in one slice is embedded into its subsequent slice. By design, the authentication tag is repeatedly but selectively embedded into various elements in a HEVC video, including nonzero DCT coefficients, QP parameter values, and prediction modes, depending on the bit segment in the generated tag. Our scheme offers three layers of authentication to detect and localize the tampered regions in a HEVC video, as well as verifying the source/sender of the video using a shared secret key. Video sequences from various classes (resolutions) are considered to verify the performance of the proposed multi-layer authentication scheme. Results show that, at the expense of slight degradation in perceptual quality, the proposed scheme is robust against several common attacks. A functional comparison is performed between the proposed multi-layer authentication scheme and the conventional schemes. [ABSTRACT FROM AUTHOR]

The general purpose no reference image quality assessment (NR-IQA) is a challenging task, which faces two hurdles: (1) it is difficult to develop one quality aware feature which works well across different types of distortion and (2) it is hard to learn a regression model to approximate a complex distribution for all training samples in the feature space. In this paper, we propose a novel NR-IQA method that addresses these problems by introducing the multi-domain structural information and piecewise regression. The main motivation of our method is based on two points. Firstly, we develop a new local image representation which extracts the structural image information from both the spatial-frequency and spatial domains. This multi-domain description could better capture human vision property. By combining our local features with a complementary global feature, the discriminative power of each single feature could be further improved. Secondly, we develop an efficient piecewise regression method to capture the local distribution of the feature space. Instead of minimizing the fitting error for all training samples, we train the specific prediction model for each query image by adaptive online learning, which focuses on approximating the distribution of the current test image’s k -nearest neighbor (KNN). Experimental results on three benchmark IQA databases (i.e., LIVE II, TID2008 and CSIQ) show that the proposed method outperforms many representative NR-IQA algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel method is proposed to detect salient regions in images. To measure pixel-level saliency, joint spatial-color constraint is defined, i.e., spatial constraint (SC), color double-opponent (CD) constraint and similarity distribution (SD) constraint. The SC constraint is designed to produce global contrast with ability to distinguish the difference between “center and surround”. The CD constraint is introduced to extract intensive contrast of red-green and blue-yellow double opponency. The SD constraint is developed to detect the salient object and its background. A two-layer structure is adopted to merge the SC, CD and SD saliency into a saliency map. In order to obtain a consistent saliency map, the region-based saliency detection is performed by incorporating a multi-scale segmentation technique. The proposed method is evaluated on two image datasets. Experimental results show that the proposed method outperforms the state-of-the-art methods on salient region detection as well as human fixation prediction. [Copyright &y& Elsevier]