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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2009.01.010 Byline: Zhe-Ming Lu (a)(b), Jun-Xiang Wang (a)(b), Bei-Bei Liu (a) Keywords: Lossless data hiding; Reversible data hiding; Vector quantization; Index residual value coding Abstract: Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding. Author Affiliation: (a) School of Information Science and Technology, Sun Yat-Sen University, XinGangXi Road 135, Haizhu District, Guangzhou 510275, PR China (b) Department of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, PR China Article History: Received 12 April 2008; Revised 15 November 2008; Accepted 9 January 2009

With the popularization of media-capture devices and the development of the Internet's basic facilities, video has become the most popular media information in recent years. The massive capacity of video imposes the demand of automatic video identification techniques which are very important to various applications such as content based video retrieval and copy detection. Therefore, as a challenging problem, video identification has drawn more and more attention in the past decade. The problem addressed here is to identify a given video clip in a given set of video sequences. In this paper, a robust video identification algorithm based on local non-negative matrix factorization (LNMF) is presented. First, some concepts about LNMF are described and the way of finding the factorized matrix is given. Then, its convergence is proven. In addition, a LNMF based shot detection method is proposed for constructing a video identification framework completely based on LNMF. Finally, a LNMF based identification approach using Hausdorff distance is introduced and a two-stage search process is proposed. Experimental results show the robustness of the proposed approach to many kinds of content-preserved distortions and its superiority to other algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Online video nowadays has become one of the top activities for users and has become easy to access. In the meantime, how to manage such huge amount of video data and retrieve them efficiently has become a big issue. In this article, we propose a novel method for video abstraction based on fast clustering of the regions of interest (ROIs). Firstly, the key-frames in each shot are extracted using the average histogram algorithm. Secondly, the saliency and edge maps are generated from each key-frame. According to these two maps, the key points for the visual attention model can be determined. Meanwhile, in order to expand the regions surrounding the key points, several thresholds are calculated from the corresponding key-frame. Thirdly, based on the key points and thresholds, several regions of interest are expanded and thus the main content in each frame is obtained. Finally, the fast clustering method is performed on the key frames by utilizing their ROIs. The performance and effectiveness of the proposed video abstraction algorithm is demonstrated by several experimental results. [Copyright &y& Elsevier]

Abstract: This paper proposes a reversible data hiding method for natural images. Due to the similarity of neighbor pixels’ values, most differences between pairs of adjacent pixels are equal or close to zero. In this work, a histogram is constructed based on these difference statistics. In the data embedding stage, a multilevel histogram modification mechanism is employed. As more peak points are used for secret bits modulation, the hiding capacity is enhanced compared with those conventional methods based on one or two level histogram modification. Moreover, as the differences concentricity around zero is improved, the distortions on the host image introduced by secret content embedding is mitigated. In the data extraction and image recovery stage, the embedding level instead of the peak points and zero points is used. Accordingly the affiliated information is much smaller than in those methods of the kind. A sequential recovery strategy is exploited for each pixel is reconstructed with the aid of its previously recovered neighbor. Experimental results and comparisons with other methods demonstrate our method''s effectiveness and superior performance. [Copyright &y& Elsevier]

• We combined 1D and 2D convolutional kernels to extract orientation-aware features. • We employed a channel attention mechanism to adaptively select informative orientation-aware features. • Progressive feature fusion scheme is proposed to fuse hierarchical features. Single image super-resolution (SISR) is an active research topic in the fields of image processing, computer vision and pattern recognition, restoring high-frequency details and textures based on the low-resolution input image. In this paper, we aim to build more accurate and faster SISR models via developing better-performing feature extraction and fusion techniques. Firstly, we proposed a novel Orientation-Aware feature extraction/selection Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i.e., 3 × 1 , 1 × 3 , and 3 × 3) for extracting orientation-aware features. The channel attention mechanism is deployed within each OAM, performing scene-specific selection of informative outputs of the orientation-dependent kernels (e.g., horizontal, vertical, and diagonal). Secondly, we present an effective fusion architecture to progressively integrate multi-scale features extracted in different convolutional stages. Instead of directly combining low-level and high-level features, similar outputs of adjacent feature extraction modules are grouped and further compressed to generate a more concise representation of a specific convolutional stage for high-accuracy SISR task. Based on the above two important improvements, we present a compact but effective CNN-based model for high-quality SISR via Progressive Fusion of Orientation-Aware features (SISR-PF-OA). Extensive experimental results verify the superiority of the proposed SISR-PF-OA model, performing favorably against the state-of-the-art models in terms of both restoration accuracy and computational efficiency (e.g., SISR-PF-OA outperforms RCAN model, achieving higher PSNR 31.25 dB vs. 31.21 dB and using fewer FLOPs 764.41 G vs. 1020.28 G on the Manga109 dataset for scale factor × 4 SISR task.). The source codes will be made publicly available. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a blind watermarking scheme based on discrete fractional random transform. The watermark information can be a binary sequence, a gray level image or a set of decimal fractions sampled from a given source signal. The host image is subsampled into four subimages, and the high correlations among their discrete fractional random transform coefficients are exploited for watermark embedding. Based on this self-reference strategy, the watermark can be extracted without the aid of the host image. As a fragile watermarking technique, our scheme can be used in tamper detection. Besides, it can be used in self-embedding for a large payload is provided. Meanwhile, security of the watermark is preserved due to the randomness of the discrete fractional random transform. Experimental results demonstrate the effectiveness of our scheme. [ABSTRACT FROM AUTHOR]