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Image retargeting technology has been widely studied to adapt images for the devices with heterogeneous screen resolutions. Meanwhile effective objective retargeting quality assessment algorithms are also very important for optimizing and selecting favorable retargeting methods. Unlike previous assessment algorithms which rely on image local structure features and unidirectional prediction of information loss, we propose a bi-directional natural salient scene distortion model (BNSSD) including image natural scene statistics (NSS) measurement, salient global structure distortion measurement, and bi-directional salient information loss measurement. First, we propose a new NSS model in log-Gabor domain and verify its effectiveness in reflecting nature scene statistical distortions introduced during the retargeting process. Second, the concept of salient global structure distortion is proposed to measure the global structure uniformity in the corresponding salient regions between original and retargeted images. Finally, we propose a bidirectional salient information loss metric to measure the information loss between salient areas in original image and retargeted image. The effectiveness of the BNSSD model is verified on two widely recognized public databases, and the experimental results show that our method outperforms the state-of-the-art algorithms under different statistical assessment criteria. [ABSTRACT FROM PUBLISHER]

We propose an adaptive hybrid digital–analog video transmission (A-HDAVT) scheme for robust video streaming in mobile networks with realistic fading channels. This scheme is fundamentally different from recent research in hybrid digital–analog video transmission in which wireless channels are unrealistically assumed to be Gaussian. For fading channels, it is critical to take full advantage of diversity in both video contents and multiuser channels. Like all hybrid approaches, A-HDAVT is designed to exploit the benefits from both digital and analog systems. To achieve this goal, each group of pictures is first transformed into one low-pass frame and several high-pass frames with motion-compensated temporal filtering. The critical low-pass frame is reliably transmitted as base layer in a digital mode, while high-pass frames are transmitted as enhancement layers in an analog mode to achieve desired graceful degradation performance. In analog transmission, we introduce a channel prediction-based adaptive power-distortion optimization (P-APDO) scheme to combat channel fading in mobile networks. The basic idea behind P-APDO is to perform power allocation based on the video content as well as the predicted channel status. Furthermore, we also investigate the multiuser scenarios in which the content diversity and channel diversity among users are appropriately exploited. Extensive simulations have been carried out to evaluate the performance of A-HDAVT under various degrees of channel fading. The results show that A-HDAVT achieves significant performance gains over competing schemes Robust Uncoded Video Transmission, Parcast, and Softcast in both single-user and multiuser scenarios. [ABSTRACT FROM PUBLISHER]

Video transmission using Scalable Video Coding (SVC) enables the functionalities of adaptation for both channel bandwidth and terminal device. However, link adaptation, which requires the video bitstream to provide error resilient scalability (ERS), is currently not supported in SVC. In this paper, we propose a robust SVC bitstream transmission framework, which integrates both error resilient (ER) coding and error concealment (EC) to enable the desired link adaptability. First, at the encoder, we design an ER coding scheme for SVC using redundant pictures, in which redundant picture information (RPI) is generated under rate-distortion criteria and transmitted to the media gateway together with the SVC bitstream. Then, ERS can be effectively achieved at the media gateway by selectively adding or removing coded pictures of different video coding layers according to the RPI and current network link status. Finally, at the decoder, we take advantage of the unique characteristics of SVC to develop an EC scheme based on both the proposed Virtual-BLSkip method and Wiener filtering to recover the lost or removed enhancement layer pictures. The proposed framework has negligible impact on the coding efficiency because it transmits RPIs rather than directly adding redundancy into the original bitstream. More importantly, it successfully strikes a balance between coding efficiency, error resiliency, and operation complexity. It is capable of providing the desired ERS with extremely low complexity, and maintaining virtually the same bit rate as input video bitstream. The experimental results show that the proposed framework achieves significant performance gains over existing ER transmission schemes. [ABSTRACT FROM PUBLISHER]

An optimal joint source-channel coding (OJSCC) scheme is developed for encoding memoryless generalized Gaussian distribution (GGD) sources and transmission over noisy channels, including memoryless channels and bursty channels. In particular, we are able to incorporate both transition probability and bit error rate of a bursty channel model into an end-to-end rate-distortion (R-D) function. This R-D function results in an optimum tradeoff between source coding accuracy and channel error protection under a fixed transmission rate. Based on the results of OJSCC, we further proposed a robust joint source-channel coding (RJSCC) scheme based on a combination of OJSCC with allpass filtering source reshaping. Experimental results based on a broad class of GGD sources suggest that the RJSCC scheme consistently outperforms the OJSCC scheme when the shape factor, a parameter of GGD, is less than 2.0. The comparison of RJSCC with the state-of-the-art robust quantization (RQ) [IEEE Trans. Image Process. 7 (1998) 496-505] shows that RJSCC is comparable with RQ at low bit rate and significantly better than RQ at high bit rate. In addition, an image transmission system using RJSCC demonstrates that the RJSCC system can be efficiently employed in practical applications. [ABSTRACT FROM AUTHOR]

Discrete wavelet transform provides an advantageous framework of multiresolution space-frequency representation with promising applications in image processing. An efficient modeling of the spatial and frequency characteristics of the wavelet transform coefficients has been recognized as a key to designing efficient quantization for wavelet-based image coding. We have proposed a Bayesian estimation framework to provide a unified, simultaneous modeling of both the spatial distribution and the intensity distribution of the wavelet transform coefficients [IEEE Trans. Circuit Syst. Video Technol. 7 (1997) 343], where the spatially localized characteristics of a given subband are modeled by Gibbs random fields (GRF). In this study, we investigate the modeling of individual cluster intensity distribution of the subband coefficients, within the context of a joint scene and signal modeling. This joint modeling decomposes the overall distribution of the coefficients into the superposition of individual cluster distributions. Among Gaussian, Laplacian, and generalized Gaussian distributions, we conclude that the composite of multiple generalized Gaussian distributions is most consistent with the overall distribution of the coefficients for any given high frequency subband. Meanwhile, modeling by Laplacian distributions has its advantage in terms of low computational complexity. The joint modeling enables us to devise a novel scene adaptive and signal adaptive quantization that fully exploits the coding redundancies resulting from wavelet transform. The concept and the mechanism of the proposed Bayesian estimation framework are applicable and tunable to other wavelet-based image processing tasks. [Copyright &y& Elsevier]

Describes a novel three-dimensional image segmentation technique capable of robust segmentation using K-mean clustering and knowledge-based morphological operations. Description of the volumetric data; Information on the adaptive K-mean clustering algorithm; Results of the segmentation; Conclusion.

Presents a scheme for artifact reduction of low bit rate discrete-cosine-transform-compressed images. Optimization technique; Calibration of coefficients; Anchor blocks and the double spiral scan; Gradient continuity constraint; Image compression model; Formulation of MAP estimation.