A novel Compressed-Sensing-based(CS-based)distributed video coding(DVC)system,called distributed Adaptive Compressed video Sensing(DISACOS),is proposed in this *** this system,the input frames are divided into key fra...
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A novel Compressed-Sensing-based(CS-based)distributed video coding(DVC)system,called distributed Adaptive Compressed video Sensing(DISACOS),is proposed in this *** this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS *** 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 *** 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 *** fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge ***,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 *** results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.
Error Estimating Code (EEC) is a new channel coding method to estimate the Bit Error Rate (BER) information of the transmitted sequence. However, the estimated BER is not precise enough if the practical value of BER i...
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Error Estimating Code (EEC) is a new channel coding method to estimate the Bit Error Rate (BER) information of the transmitted sequence. However, the estimated BER is not precise enough if the practical value of BER is high. A weighted EEC estimation method is proposed to improve the accuracy performance of BER estimation by classifying the raw estimation results into intervals and multiplying them by different coefficients separately. The applications of weighted EEC in modulation selection scheme and distributed video coding are discussed. Simulation results show that the EEC-based modulation selection method can achieve better performance at a cost of little redundancy and computation, and the EEC-based rate estimation method in distributed video coding can save the decoding time.
videocoding and compression are essential components of multimedia services but are known to be computationally intensive and energy demanding. Traditional videocoding paradigms, predictive and distributedvideo cod...
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videocoding and compression are essential components of multimedia services but are known to be computationally intensive and energy demanding. Traditional videocoding paradigms, predictive and distributed video coding (PVC and DVC), result in excessive computation at either the encoder (PVC) or decoder (DVC). Several recent papers have proposed a hybrid PVC/DVC codec which shares the videocoding workload between encoder and decoder. In this article, we propose a controller for such hybrid coders that considers energy and temperature to dynamically split the coding workload of a system comprised of one encoder and one decoder. We also present two heuristic algorithms for determining safe operating temperatures in the controller solution: (1) stable state thermal modeling algorithm, which focuses on long term temperatures, and (2) transient thermal modeling algorithm, which is better for short-term thermal behavior. Results show that the proposed algorithms result in more balanced energy utilization, improve overall system lifetime, and reduce operating temperatures when compared to strictly PVC and DVC systems.
Compressed sensing theory based distributed video coding is one of the active topics in videocoding research filed. In the compressed perception theory, the KSVD dictionary is usually used to achieve signal sparsity....
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Compressed sensing theory based distributed video coding is one of the active topics in videocoding research filed. In the compressed perception theory, the KSVD dictionary is usually used to achieve signal sparsity. In this paper,Based on the K-SVD algorithm, this paper proposes to apply the discriminative K-SVD (d-ksvd) algorithm to distributed compressed sensing videocoding. We evaluate our method using the video in YUV format. It is verified by experiments that our method not only achieve better result of reconstruction but also save lot of time in terms of computing time.
This paper addresses the problem of high-computational complexity decoding in traditional Wyner-Ziv videocoding (WZVC). The key focus is the migration of two traditionally high-computationally complex encoder algorit...
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This paper addresses the problem of high-computational complexity decoding in traditional Wyner-Ziv videocoding (WZVC). The key focus is the migration of two traditionally high-computationally complex encoder algorithms, namely motion estimation and mode decision. In order to reduce the computational burden in this process, the proposed architecture adopts the partial boundary matching algorithm and four flexible types of block mode decision at the decoder. This approach does away with the need for motion estimation and mode decision at the encoder. The experimental results show that the proposed padding block-based WZVC not only decreases decoder complexity to approximately one hundredth that of the state-of-the-art DISCOVER decoding but also outperforms DISCOVER codec by up to 3 to 4 dB. (C) 2017 SPIE and IS&T
distributedvideo codec (DVC) has been developed to construct a simple encoder that utilizes information theory for distributed sources in the circumstance of mobile multimedia communication. In the DVC codec, an effi...
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distributedvideo codec (DVC) has been developed to construct a simple encoder that utilizes information theory for distributed sources in the circumstance of mobile multimedia communication. In the DVC codec, an efficient algorithm to generate side information (SI) is one of the most important techniques to improve the coding performance. We propose a scheme to increase the quality of SI frame, where the proposed scheme consists of three steps. In the first step, SI frame is constructed by motion estimation and motion compensation in the DVC decoder. Then, in the second step, the blocks in the temporary SI frame are classified into reliable or unreliable ones. The unreliable blocks are updated by block boundary matching algorithm in the third step. Simulation results show that the proposed algorithm outperforms the conventional methods significantly. In addition, the proposed scheme can be combined with the conventional schemes generating SI frame to increase the coding performance of the DVC codec. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Light-weight videocoding (LVC) follows distributed video coding (DVC) and designs to move computational complexity from the encoder to the decoder, thus making a low computational complexity encoder. In traditional v...
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Light-weight videocoding (LVC) follows distributed video coding (DVC) and designs to move computational complexity from the encoder to the decoder, thus making a low computational complexity encoder. In traditional videocoding, the high computational complexity encoder algorithms, where motion estimation and mode decision, are the main transferred objects. In order to alleviate the computational burden, the proposed architecture adopts the Partial Boundary Matching Algorithm (PBMA) and four flexible types of mode decision at the decoder;this circumvents the traditional use of motion estimation and mode decision at the encoder. In simulation, the proposed architecture, Padding Block-based LVC, not only outperforms the state-of-the-art DVC (DISCOVER) codec by up to 4 similar to 5 dB but also significantly decreases decoder complexity to approximately one hundred times lower than that of the DISCOVER codec.
Areas with social and business impact such as entertainment, healthcare, surveillance, and e-learning would benefit from improvements in videocoding and transcoding services. New codecs, such as AV1, are being develo...
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Areas with social and business impact such as entertainment, healthcare, surveillance, and e-learning would benefit from improvements in videocoding and transcoding services. New codecs, such as AV1, are being developed to deal with new demands for high video resolutions with bandwidth constraints and quality requirements. However, these new codecs have high computational requirements and new strategies are needed to accelerate their processing. Cloud computing offers interesting features such as on-demand resource allocation, multitenancy, elasticity, and resiliency among others. Deploying videocoding and transcoding services on these infrastructures is suitable because it allows the adaptation of the resources to the workload, offers high availability, and provides ubiquitous access. This work proposes a cloud-based distributed architecture, tuned for videocoding, that relies on an elastic pool of workers and media servers to provide fault-tolerance. A distributed application is deployed on the top of the architecture to split the video encoding process of a video in several jobs that can be dynamically assigned among the elastic pool of workers. The proposed solution is analyzed in terms of scalability, resource usage, and job distribution varying the number of workers for three well-known video codecs: H.265, VP9, and AV1. Moreover, the quality of the encoded videos has been evaluated for different bit rates and number of frames per job using full reference metrics like: PSNR, MS-SSIM, and VIF. Results show that our solution achieve similar quality and bitrate compared with full videocoding while improving the total encoding time, which can decrease more than 90% depending on the encoder and the number of workers.
This study presents a novel distributed soft video delivery scheme using hybrid digital and analog framework, which realizes a relatively lightweight encoder as well as good robustness. Specifically, after applying sc...
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This study presents a novel distributed soft video delivery scheme using hybrid digital and analog framework, which realizes a relatively lightweight encoder as well as good robustness. Specifically, after applying scalar quantization to the interframes, the scaled analog information (quantization error) and the scaled digital information (quantized source) are superimposed and then transmitted. In this way, the proposed scheme directly delivers pseudo-analog symbols over the orthogonal frequency division multiplexing (OFDM) channels and involves no complicated digital codec. Moreover, we utilize cross-frame correlation to implement a distributed paradigm which further reduces the encoder complexity since the complex motion estimation and compensation algorithms are transferred to the decoder. Accordingly, the power distortion optimization scheme, which resolve the allocation of power and the parameters optimization to achieve minimum transmission distortion, is proposed. To solve it, first, we formulate the power distortion expressions regarding quantization parameters and power allocation coefficients. Subsequently, we divide the problem into two sub-problems based on the fast coordinate descent method and further propose a greedy iterative algorithm to optimize them. We also develop a data-driven optimization algorithm based on deep learning that reduces the additional delay brought by the iterative optimization method. At the receiver, we estimate the quantization output and quantization error by the modified linear least squares estimation with the virtual noise variance. Based on the simulation results, the proposed framework has a better performance in terms of peak signal-to-noise ratio and structural similarity than the relevant cutting-edge schemes while maintaining good robustness.
Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new videocoding algorithm based on distributed Compressive Sampling(DCS) principles, where almost...
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Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new videocoding algorithm based on distributed Compressive Sampling(DCS) principles, where almost all computation burdens can be shifted to the decoder, resulting in a very lowcomplexity encoder. At the decoder, compressed video can be efficiently reconstructed. Our algorithm can be useful in those video applications that require very low complex encoders. Simulation results show that our scheme compares favorably with existing schemes at a much lower implementation cost.
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