The Experts below are selected from a list of 5670 Experts worldwide ranked by ideXlab platform

Michael G Strintzis - One of the best experts on this subject based on the ideXlab platform.

  • wireless image transmission using turbo codes and optimal unequal error protection
    IEEE Transactions on Image Processing, 2005
    Co-Authors: Nikolaos Thomos, N V Boulgouris, Michael G Strintzis
    Abstract:

    A novel image transmission scheme is proposed for the communication of set partitioning in hierarchical trees image streams over wireless channels. The proposed scheme employs turbo codes and Reed-Solomon codes in order to deal effectively with burst errors. An algorithm for the optimal unequal error protection of the Compressed Bitstream is also proposed and applied in conjunction with an inherently more efficient technique for product code decoding. The resulting scheme is tested for the transmission of images over wireless channels. Experimental evaluation clearly demonstrates the superiority of the proposed transmission system in comparison to well-known robust coding schemes.

  • wireless image transmission using turbo codes and optimal unequal error protection
    International Conference on Image Processing, 2003
    Co-Authors: Nikolaos Thomos, N V Boulgouris, Michael G Strintzis
    Abstract:

    A novel image transmission scheme is proposed for the communication of SPIHT image streams over wireless channels. The proposed scheme employs turbo codes and erasure-correction codes in order to deal effectively with burst errors. An algorithm for the optimal unequal error protection of the Compressed Bitstream is also proposed. The resulting scheme is tested for the transmission of images over wireless channels. Experimental evaluation clearly demonstrates the superiority of the proposed scheme in comparison to well-known robust coding schemes.

Gao Wen - One of the best experts on this subject based on the ideXlab platform.

  • Compression artifact reduction by overlapped-block transform coefficient estimation with block similarity
    2013
    Co-Authors: Zhang Xinfeng, Xiong Ruiqin, Fan Xiaopeng, Ma Siwei, Gao Wen
    Abstract:

    Block transform coded images usually suffer from annoying artifacts at low bit rates, caused by the coarse quantization of transform coefficients. In this paper, we propose a new method to reduce compression artifacts by the overlapped-block transform coefficient estimation from non-local blocks. In the proposed method, the discrete cosine transform coefficients of each block are estimated by adaptively fusing two prediction values based on their reliabilities. One prediction is the quantized values of coefficients decoded from the Compressed Bitstream, whose reliability is determined by quantization steps. The other prediction is the weighted average of the coefficients in nonlocal blocks, whose reliability depends on the variance of the coefficients in these blocks. The weights are used to distinguish the effectiveness of the coefficients in nonlocal blocks to predict original coefficients and are determined by block similarity in transform domain. To solve the optimization problem, the overlapped blocks are divided into several subsets. Each subset contains nonoverlapped blocks covering the whole image and is optimized independently. Therefore, the overall optimization is reduced to a set of sub-optimization problems, which can be easily solved. Finally, we provide a strategy for parameter selection based on the compression levels. Experimental results show that the proposed method can remarkably reduce compression artifacts and significantly improve both the subjective and objective qualities of block transform coded images. © 1992-2012 IEEE

  • Compression Artifact Reduction by Overlapped-Block Transform Coefficient Estimation With Block Similarity
    ieee transactions on image processing, 2013
    Co-Authors: Zhang Xinfeng, Xiong Ruiqin, Fan Xiaopeng, Ma Siwei, Gao Wen
    Abstract:

    Block transform coded images usually suffer from annoying artifacts at low bit rates, caused by the coarse quantization of transform coefficients. In this paper, we propose a new method to reduce compression artifacts by the overlapped-block transform coefficient estimation from non-local blocks. In the proposed method, the discrete cosine transform coefficients of each block are estimated by adaptively fusing two prediction values based on their reliabilities. One prediction is the quantized values of coefficients decoded from the Compressed Bitstream, whose reliability is determined by quantization steps. The other prediction is the weighted average of the coefficients in nonlocal blocks, whose reliability depends on the variance of the coefficients in these blocks. The weights are used to distinguish the effectiveness of the coefficients in nonlocal blocks to predict original coefficients and are determined by block similarity in transform domain. To solve the optimization problem, the overlapped blocks are divided into several subsets. Each subset contains nonoverlapped blocks covering the whole image and is optimized independently. Therefore, the overall optimization is reduced to a set of sub-optimization problems, which can be easily solved. Finally, we provide a strategy for parameter selection based on the compression levels. Experimental results show that the proposed method can remarkably reduce compression artifacts and significantly improve both the subjective and objective qualities of block transform coded images.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000325223300004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceEngineering, Electrical & ElectronicSCI(E)EI36ARTICLE124613-46262

Nikolaos Thomos - One of the best experts on this subject based on the ideXlab platform.

  • wireless image transmission using turbo codes and optimal unequal error protection
    IEEE Transactions on Image Processing, 2005
    Co-Authors: Nikolaos Thomos, N V Boulgouris, Michael G Strintzis
    Abstract:

    A novel image transmission scheme is proposed for the communication of set partitioning in hierarchical trees image streams over wireless channels. The proposed scheme employs turbo codes and Reed-Solomon codes in order to deal effectively with burst errors. An algorithm for the optimal unequal error protection of the Compressed Bitstream is also proposed and applied in conjunction with an inherently more efficient technique for product code decoding. The resulting scheme is tested for the transmission of images over wireless channels. Experimental evaluation clearly demonstrates the superiority of the proposed transmission system in comparison to well-known robust coding schemes.

  • wireless image transmission using turbo codes and optimal unequal error protection
    International Conference on Image Processing, 2003
    Co-Authors: Nikolaos Thomos, N V Boulgouris, Michael G Strintzis
    Abstract:

    A novel image transmission scheme is proposed for the communication of SPIHT image streams over wireless channels. The proposed scheme employs turbo codes and erasure-correction codes in order to deal effectively with burst errors. An algorithm for the optimal unequal error protection of the Compressed Bitstream is also proposed. The resulting scheme is tested for the transmission of images over wireless channels. Experimental evaluation clearly demonstrates the superiority of the proposed scheme in comparison to well-known robust coding schemes.

Alan C. Bovik - One of the best experts on this subject based on the ideXlab platform.

  • Human Visual System Based Scalable Video Coding and Communications
    2016
    Co-Authors: Zhou Wang, Jack Kouloheris, Alan C. Bovik
    Abstract:

    This paper introduces our recent research work on the development of a scalable foveated visual information coding and communication system, which follows two emerging trends in visual communication research. One is to design rate scalable image and video codecs, which allow the extraction of coded visual information at contin-uously varying bit rates from a single Compressed Bitstream. The other is to incorporate human visual system models to improve the state-of-the-art of image and video coding techniques by better exploiting the properties of the intended receiver. The central idea of the proposed system is to organize the encoded Bitstream to provide the best decoded visual information at an arbitrary bit rate in terms of foveated visual quality measurement. Such a scalable foveated visual information processing system has many potential applications in the eld of vi-sual communications. Signicant examples include network image browsing, network videoconferencing, robust visual communication over noisy channels, and visual communication over active networks

  • foveation scalable video coding with automatic fixation selection
    IEEE Transactions on Image Processing, 2003
    Co-Authors: Zhou Wang, Alan C. Bovik
    Abstract:

    Image and video coding is an optimization problem. A successful image and video coding algorithm delivers a good tradeoff between visual quality and other coding performance measures, such as compression, complexity, scalability, robustness, and security. In this paper, we follow two recent trends in image and video coding research. One is to incorporate human visual system (HVS) models to improve the current state-of-the-art of image and video coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate scalable image and video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single Compressed Bitstream. Specifically, we propose a foveation scalable video coding (FSVC) algorithm which supplies good quality-compression performance as well as effective rate scalability. The key idea is to organize the encoded Bitstream to provide the best decoded video at an arbitrary bit rate in terms of foveated visual quality measurement. A foveation-based HVS model plays an important role in the algorithm. The algorithm is adaptable to different applications, such as knowledge-based video coding and video communications over time-varying, multiuser and interactive networks.

Ruiqin Xiong - One of the best experts on this subject based on the ideXlab platform.

  • compression artifact reduction by overlapped block transform coefficient estimation with block similarity
    IEEE Transactions on Image Processing, 2013
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong
    Abstract:

    Block transform coded images usually suffer from annoying artifacts at low bit rates, caused by the coarse quantization of transform coefficients. In this paper, we propose a new method to reduce compression artifacts by the overlapped-block transform coefficient estimation from non-local blocks. In the proposed method, the discrete cosine transform coefficients of each block are estimated by adaptively fusing two prediction values based on their reliabilities. One prediction is the quantized values of coefficients decoded from the Compressed Bitstream, whose reliability is determined by quantization steps. The other prediction is the weighted average of the coefficients in nonlocal blocks, whose reliability depends on the variance of the coefficients in these blocks. The weights are used to distinguish the effectiveness of the coefficients in nonlocal blocks to predict original coefficients and are determined by block similarity in transform domain. To solve the optimization problem, the overlapped blocks are divided into several subsets. Each subset contains nonoverlapped blocks covering the whole image and is optimized independently. Therefore, the overall optimization is reduced to a set of sub-optimization problems, which can be easily solved. Finally, we provide a strategy for parameter selection based on the compression levels. Experimental results show that the proposed method can remarkably reduce compression artifacts and significantly improve both the subjective and objective qualities of block transform coded images.