With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communica...With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communication.Multi-view image compression aims to improve compression efficiency by leveraging correlations between images.However,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained devices.In this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding.Our model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image reconstruction.The encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy coding.The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side.Specifically,we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps.Second,we employ a semi-coupled convolution module to extract both shared and specific information in images.Finally,a cross-attention module is employed to fuse mutual information extracted from side information.The effectiveness of our model is validated on various datasets,including KITTI Stereo and Cityscapes.The results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.展开更多
The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side inf...The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side information generation algorithm which is integrated into DVC system. First, forward motion estimation (FME) and bidirectional motion estimation (BME) on the basis of variable block size HME algorithm are used to acquire relatively accurate motion vectors. Second, a motion vector filter (MVF) is i...展开更多
In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of...In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.展开更多
Spectrum access approach and power allocation scheme are important techniques in cognitive radio(CR) system,which not only affect communication performance of CR user(secondary user,SU) but also play decisive role for...Spectrum access approach and power allocation scheme are important techniques in cognitive radio(CR) system,which not only affect communication performance of CR user(secondary user,SU) but also play decisive role for protection of primary user(PU).In this study,we propose a power allocation scheme for SU based on the status sensing of PU in a single-input single-output(SISO) CR network.Instead of the conventional binary primary transmit power strategy,namely the sensed PU has only present or absent status,we consider a more practical scenario when PU transmits with multiple levels of power and quantized side information known by SU in advance as a primary quantized codebook.The secondary power allocation scheme to maximize the average throughput under the rate loss constraint(RLC) of PU is parameterized by the sensing results for PU,the primary quantized codebook and the channel state information(CSI) of SU.Furthermore,Differential Evolution(DE) algorithm is used to solve this non-convex power allocation problem.Simulation results show the performance and effectiveness of our proposed scheme under more practical communication conditions.展开更多
Distributed video coding （DVC） is a new video coding approach based on Wyner-Ziv theorem. The novel uplink-friendly DVC, which offers low-complexity, low-power consuming, and low-cost video encoding, has aroused mor...Distributed video coding （DVC） is a new video coding approach based on Wyner-Ziv theorem. The novel uplink-friendly DVC, which offers low-complexity, low-power consuming, and low-cost video encoding, has aroused more and more research interests. In this paper a new method based on multiple view geometry is presented for spatial side information generation of uncalibrated video sensor network. Trifocal tensor encapsulates all the geometric relations among three views that are independent of scene structure; it can be computed from image correspondences alone without requiring knowledge of the motion or calibration. Simulation results show that trifocal tensor-based spatial side information improves the rate-distortion performance over motion compensation based interpolation side information by a maximum gap of around 2dB. Then fusion merges the different side information （temporal and spatial） in order to improve the quality of the final one. Simulation results show that the rate-distortion gains about 0.4 dB.展开更多
Side information （SI） is one of the key issues in distributed video coding （DVC） and affects the compression performance of DVC largely. This paper proposes an SI refinement algorithm, in which the Wyner-Ziv （WZ...Side information （SI） is one of the key issues in distributed video coding （DVC） and affects the compression performance of DVC largely. This paper proposes an SI refinement algorithm, in which the Wyner-Ziv （WZ） frame is split into two parts based on checkerboard pattern, and the two parts are encoded independently but decoded sequentially. In the decoding process, the part 1 is first decoded with the initial SI and partially decoded part （PDP） 1 is used to improve the motion vectors （MVs） and SI of both parts. At the next stage, the part 2 is decoded with the improved SI and PDP 2 is used to further refine MVs of the part 2. Then, SI of both parts are further refined. Simulation results show that the proposed algorithm can improve the peak signal to noise ratio （PSNR） by up to 1.43 dB when compared with traditional DVC codec.展开更多
Side information has a significant influence on the rate-distortion（RD） performance of distributed video coding（DVC）. In the conventional motion compensated frame interpolation scheme, all blocks adopt the same si...Side information has a significant influence on the rate-distortion（RD） performance of distributed video coding（DVC）. In the conventional motion compensated frame interpolation scheme, all blocks adopt the same side-information generation method regardless of the motion intensity inequality at different regions. In this paper, an improved method is proposed. The image blocks are classified into two modes, fast motion and slow motion, by simply computing the discrete cosine transformation（DCT） coefficients at the encoder. On the decoder, it chooses the direct interpolation and refined motion compensated interpolation correspondingly to generate side information. Experimental results show that the proposed method, without increasing the encoder complexity, can increase the average peak signal-to-noise ratio（PSNR） by up to 1~ 2 dB compared with the existing algorithm. Meanwhile, the proposed algorithm significantly improves the subjective quality of the side information.展开更多
Purpose–Constrained clustering is an important recent development in clustering literature.The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of backgrou...Purpose–Constrained clustering is an important recent development in clustering literature.The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background knowledge.The purpose of this paper is to suggest a new perspective for constrained clustering,by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must-and cannot-link constraints.Design/methodology/approach–Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target space.Unlike previous efforts,the author presents a non-linear method for constraint clustering,whose basic idea is to use different non-linear functions for each dimension in target space.Findings–The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linearfunctions for each dimension in target space.The proposed method for a particular case is formulated and explained for quadratic functions.To reduce the number of optimization parameters,the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to solve.Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.Originality/value–This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel functions.This work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.展开更多
In this paper,we investigate the power and subcarrier allocation issue in the case of partial side information for downlink orthogonal frequency division multiple access （OFDMA） system.Relaxation method is utilized ...In this paper,we investigate the power and subcarrier allocation issue in the case of partial side information for downlink orthogonal frequency division multiple access （OFDMA） system.Relaxation method is utilized to characterize the necessary conditions of the optimal solution and the uniqueness of the optimal solution is proved.The game theoretical concept,surplus function is also introduced to analyze the optimal solution.Based on the theoretical analysis,we propose iterative surplus balancing algorithm （ISBA） that can jointly assign the power and subcarriers in multiple rounds,and then the optimality of ISBA is proved.Simulation results are presented to show the characteristics of the theoretical analysis and ISBA.展开更多
Purpose-This paper addresses the limitations of current graph neural network-based recommendation systems,which often neglect the integration of side information and the modeling of complex high-order interactions amo...Purpose-This paper addresses the limitations of current graph neural network-based recommendation systems,which often neglect the integration of side information and the modeling of complex high-order interactions among nodes.The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data.We propose a novel method called MSHCN,which leverages hypergraph neural networks to integrate side information and model complex interactions,thereby improving user and item representations.Design/methodology/approach-The MSHCN method employs a hypergraph structure to incorporate various types of side information,including social relationships among users and item attributes,which are essential for enriching user and item representations.The k-means clustering algorithm is utilized to create item-associated hypergraphs,while sentiment analysis on user reviews refines the modeling of user interests.Additionally,hypergraphs are constructed for user-user and item-item interactions based on interaction similarity.MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.Findings-Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models,particularly in its ability to capture and utilize side information and high-order interactions.This results in superior user and item representations and improved recommendation performance.Originality/value-The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions.The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models,providing a significant enhancement in recommendation accuracy.展开更多
The Wyner-Ziv Problem for general sources with average distortion under fixed-length coding is investigated in this paper. To solve the problem,an enhanced covering lemma for a Markov chain is first established. Then ...The Wyner-Ziv Problem for general sources with average distortion under fixed-length coding is investigated in this paper. To solve the problem,an enhanced covering lemma for a Markov chain is first established. Then based on the lemma,a general formula for the rate-distortion function of the problem is derived,where the distortion is only assumed uniformly bounded and may be nonadditive. Finally,it is further pointed out that such methods can be used to establish more general results on multiterminal source coding problems.展开更多
In the Wyner-Ziv(WZ) video coding paradigm, a virtual correlation channel is assumed between the quantized source and the side information(SI) at the decoder, and channel coding is applied to achieve compression. In t...In the Wyner-Ziv(WZ) video coding paradigm, a virtual correlation channel is assumed between the quantized source and the side information(SI) at the decoder, and channel coding is applied to achieve compression. In this paper, errors caused by the virtual correlation channel are addressed and an error concealment approach is proposed for pixel-based WZ video coding. In the approach, errors after decoding are classified into two types. Type 1 errors are caused by residual bit errors after channel decoding, while type 2 errors are due to low quality of SI in part of a frame which causes SI not lying within the quantization bin of a decoded quantized pixel value. Two separate strategies are respectively designed to detect and conceal the two types of errors. Simulations are carried out and results are presented to demonstrate the effectiveness of the proposed approach.展开更多
A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this paper.In this system,the input frames are divided into ke...A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this paper.In this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS sampling.The key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber(SPL)algorithm using multi-hypothesis predictions.For the non-key frames,a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information(SI)generated by the conventional interpolation or extrapolation at the decoder;then,another group of CS measurements are sampled again upon the decoder’s request.To fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge features.Finally,the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the SI.Experimental results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.展开更多
A new selected mapping(SLM)scheme based on constellation rotation is proposed to reduce the peak-to-average power ratio(PAPR)of orthogonal frequency division multiplexing(OFDM)signals.Its core idea is to generate abun...A new selected mapping(SLM)scheme based on constellation rotation is proposed to reduce the peak-to-average power ratio(PAPR)of orthogonal frequency division multiplexing(OFDM)signals.Its core idea is to generate abundant candidate signals by rotating different sub-signals of the original frequency signal with different angles.This new signal generation method can simplify the calculation process of candidate time signals into the linear addition of some intermediate signals,which are generated by the inverse fast Fourier transform(IFFT)operation of the original frequency signal.This feature can effectively reduce the computational complexity of candidate signal generation process.And compared to the traditional SLM scheme,the number of complex multiplication and complex addition of new scheme can separately be decreased by about 99.99% and 91.7% with some specific parameters.Moreover,with the help of the constellation detection mechanism at the receiver,there is no need to carry any side information at the transmitter.The simulation results show that,with the same channel transmission performance,the PAPR reduction performance of new scheme can approach or even exceed the upper bound of the traditional SLM scheme,which uses all the vectors in Hadamard matrix as the phase sequences.展开更多
基金supported by the National Natural Science Foundation of China(Key Program)(No.11932013)the Tianjin Science and Technology Plan Project(No.22PTZWHZ00040)。
文摘With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communication.Multi-view image compression aims to improve compression efficiency by leveraging correlations between images.However,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained devices.In this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding.Our model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image reconstruction.The encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy coding.The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side.Specifically,we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps.Second,we employ a semi-coupled convolution module to extract both shared and specific information in images.Finally,a cross-attention module is employed to fuse mutual information extracted from side information.The effectiveness of our model is validated on various datasets,including KITTI Stereo and Cityscapes.The results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.
基金National Natural Science Foundation of China (60702012)
文摘The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side information generation algorithm which is integrated into DVC system. First, forward motion estimation (FME) and bidirectional motion estimation (BME) on the basis of variable block size HME algorithm are used to acquire relatively accurate motion vectors. Second, a motion vector filter (MVF) is i...
基金Project(2006C11200) supported by the Science and Technology Project of Zhejiang Province of China
文摘In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.
基金supported by the National Natural Science Foundation of China(Grant No.61571209)
文摘Spectrum access approach and power allocation scheme are important techniques in cognitive radio(CR) system,which not only affect communication performance of CR user(secondary user,SU) but also play decisive role for protection of primary user(PU).In this study,we propose a power allocation scheme for SU based on the status sensing of PU in a single-input single-output(SISO) CR network.Instead of the conventional binary primary transmit power strategy,namely the sensed PU has only present or absent status,we consider a more practical scenario when PU transmits with multiple levels of power and quantized side information known by SU in advance as a primary quantized codebook.The secondary power allocation scheme to maximize the average throughput under the rate loss constraint(RLC) of PU is parameterized by the sensing results for PU,the primary quantized codebook and the channel state information(CSI) of SU.Furthermore,Differential Evolution(DE) algorithm is used to solve this non-convex power allocation problem.Simulation results show the performance and effectiveness of our proposed scheme under more practical communication conditions.
文摘Distributed video coding （DVC） is a new video coding approach based on Wyner-Ziv theorem. The novel uplink-friendly DVC, which offers low-complexity, low-power consuming, and low-cost video encoding, has aroused more and more research interests. In this paper a new method based on multiple view geometry is presented for spatial side information generation of uncalibrated video sensor network. Trifocal tensor encapsulates all the geometric relations among three views that are independent of scene structure; it can be computed from image correspondences alone without requiring knowledge of the motion or calibration. Simulation results show that trifocal tensor-based spatial side information improves the rate-distortion performance over motion compensation based interpolation side information by a maximum gap of around 2dB. Then fusion merges the different side information （temporal and spatial） in order to improve the quality of the final one. Simulation results show that the rate-distortion gains about 0.4 dB.
基金Supported by the National Natural Science Foundation of China （ No. 60736043, 60672088） and the National Basic Research Program of China （No. 2009CB32005）.
文摘Side information （SI） is one of the key issues in distributed video coding （DVC） and affects the compression performance of DVC largely. This paper proposes an SI refinement algorithm, in which the Wyner-Ziv （WZ） frame is split into two parts based on checkerboard pattern, and the two parts are encoded independently but decoded sequentially. In the decoding process, the part 1 is first decoded with the initial SI and partially decoded part （PDP） 1 is used to improve the motion vectors （MVs） and SI of both parts. At the next stage, the part 2 is decoded with the improved SI and PDP 2 is used to further refine MVs of the part 2. Then, SI of both parts are further refined. Simulation results show that the proposed algorithm can improve the peak signal to noise ratio （PSNR） by up to 1.43 dB when compared with traditional DVC codec.
基金supported by the National Natural Science Foundation of China(61071093)the Hi-Tech Research and Development Program of China(2010AA701202)+4 种基金the Sweden-Asian International Cooperation Project(348-2008-6212)the Jiangsu Province Major Technology Support Program(BE2012849)the Jiangsu Province IOT Application Demonstration Project(SJ212025)the SRF for ROCS,SEM(NJ209002)the Jiangsu Province Scientific and Technological Innovation Projects(CXLX12_0481)
文摘Side information has a significant influence on the rate-distortion（RD） performance of distributed video coding（DVC）. In the conventional motion compensated frame interpolation scheme, all blocks adopt the same side-information generation method regardless of the motion intensity inequality at different regions. In this paper, an improved method is proposed. The image blocks are classified into two modes, fast motion and slow motion, by simply computing the discrete cosine transformation（DCT） coefficients at the encoder. On the decoder, it chooses the direct interpolation and refined motion compensated interpolation correspondingly to generate side information. Experimental results show that the proposed method, without increasing the encoder complexity, can increase the average peak signal-to-noise ratio（PSNR） by up to 1~ 2 dB compared with the existing algorithm. Meanwhile, the proposed algorithm significantly improves the subjective quality of the side information.
文摘Purpose–Constrained clustering is an important recent development in clustering literature.The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background knowledge.The purpose of this paper is to suggest a new perspective for constrained clustering,by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must-and cannot-link constraints.Design/methodology/approach–Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target space.Unlike previous efforts,the author presents a non-linear method for constraint clustering,whose basic idea is to use different non-linear functions for each dimension in target space.Findings–The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linearfunctions for each dimension in target space.The proposed method for a particular case is formulated and explained for quadratic functions.To reduce the number of optimization parameters,the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to solve.Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.Originality/value–This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel functions.This work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.
基金supported by Sino-Swedish International Mobile Telecommunications-Advanced (IMT-A) Cooperation Project (2008DFA11780)Canada-China Scientific and Technological Cooperation (2010DFA11320)+1 种基金National Natural Science Foundation of China (60802033, 60873190)the Research and Development Program Hi-Tech of China (2008AA01Z211)
文摘In this paper,we investigate the power and subcarrier allocation issue in the case of partial side information for downlink orthogonal frequency division multiple access （OFDMA） system.Relaxation method is utilized to characterize the necessary conditions of the optimal solution and the uniqueness of the optimal solution is proved.The game theoretical concept,surplus function is also introduced to analyze the optimal solution.Based on the theoretical analysis,we propose iterative surplus balancing algorithm （ISBA） that can jointly assign the power and subcarriers in multiple rounds,and then the optimality of ISBA is proved.Simulation results are presented to show the characteristics of the theoretical analysis and ISBA.
文摘Purpose-This paper addresses the limitations of current graph neural network-based recommendation systems,which often neglect the integration of side information and the modeling of complex high-order interactions among nodes.The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data.We propose a novel method called MSHCN,which leverages hypergraph neural networks to integrate side information and model complex interactions,thereby improving user and item representations.Design/methodology/approach-The MSHCN method employs a hypergraph structure to incorporate various types of side information,including social relationships among users and item attributes,which are essential for enriching user and item representations.The k-means clustering algorithm is utilized to create item-associated hypergraphs,while sentiment analysis on user reviews refines the modeling of user interests.Additionally,hypergraphs are constructed for user-user and item-item interactions based on interaction similarity.MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.Findings-Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models,particularly in its ability to capture and utilize side information and high-order interactions.This results in superior user and item representations and improved recommendation performance.Originality/value-The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions.The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models,providing a significant enhancement in recommendation accuracy.
基金Project supported by the National Natural Science Foundation of China (Nos. 60472079 and 60602023) China Postdoctoral Sci-ence Foundation (No. 20060400314)
文摘The Wyner-Ziv Problem for general sources with average distortion under fixed-length coding is investigated in this paper. To solve the problem,an enhanced covering lemma for a Markov chain is first established. Then based on the lemma,a general formula for the rate-distortion function of the problem is derived,where the distortion is only assumed uniformly bounded and may be nonadditive. Finally,it is further pointed out that such methods can be used to establish more general results on multiterminal source coding problems.
基金Supported by the National Science and Technology Major Project of China(No.2018ZX10734401-004)
文摘In the Wyner-Ziv(WZ) video coding paradigm, a virtual correlation channel is assumed between the quantized source and the side information(SI) at the decoder, and channel coding is applied to achieve compression. In this paper, errors caused by the virtual correlation channel are addressed and an error concealment approach is proposed for pixel-based WZ video coding. In the approach, errors after decoding are classified into two types. Type 1 errors are caused by residual bit errors after channel decoding, while type 2 errors are due to low quality of SI in part of a frame which causes SI not lying within the quantization bin of a decoded quantized pixel value. Two separate strategies are respectively designed to detect and conceal the two types of errors. Simulations are carried out and results are presented to demonstrate the effectiveness of the proposed approach.
基金supported by the Graduate Student Research Innovation Project of Jiangsu Province China under Grants No. CXZZ12_0466, No. CXZZ11_0390the National Natural Science Foundation of China under Grants No. 61071091, No. 61271240+2 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province China under Grant No. 12KJB510019the Nanjing University of Posts and Telecommunications Natural Science Foundation under Grant No. NY212015the Technology Research Program of Hubei Provincial Department of Education under Grant No. D20121408
文摘A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this paper.In this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS sampling.The key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber(SPL)algorithm using multi-hypothesis predictions.For the non-key frames,a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information(SI)generated by the conventional interpolation or extrapolation at the decoder;then,another group of CS measurements are sampled again upon the decoder’s request.To fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge features.Finally,the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the SI.Experimental results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.
文摘A new selected mapping(SLM)scheme based on constellation rotation is proposed to reduce the peak-to-average power ratio(PAPR)of orthogonal frequency division multiplexing(OFDM)signals.Its core idea is to generate abundant candidate signals by rotating different sub-signals of the original frequency signal with different angles.This new signal generation method can simplify the calculation process of candidate time signals into the linear addition of some intermediate signals,which are generated by the inverse fast Fourier transform(IFFT)operation of the original frequency signal.This feature can effectively reduce the computational complexity of candidate signal generation process.And compared to the traditional SLM scheme,the number of complex multiplication and complex addition of new scheme can separately be decreased by about 99.99% and 91.7% with some specific parameters.Moreover,with the help of the constellation detection mechanism at the receiver,there is no need to carry any side information at the transmitter.The simulation results show that,with the same channel transmission performance,the PAPR reduction performance of new scheme can approach or even exceed the upper bound of the traditional SLM scheme,which uses all the vectors in Hadamard matrix as the phase sequences.
