The problem of robust communication of predictive encoded video in a joint source-channel setting is addressed. Specifically, the problem of predictive mismatch, where there is a drift between the state of the encoder...
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The problem of robust communication of predictive encoded video in a joint source-channel setting is addressed. Specifically, the problem of predictive mismatch, where there is a drift between the state of the encoder and the decoder is addressed as a variant of the Wyner-Ziv problem. A video encoding algorithm based on the H.26L video codec, which presents the propagation of error in predictively encoded video in the event of predictive mismatch (or drift) between the encoder and the decoder, is proposed. One of the main advantages of the proposed approach is that there is minimal loss in performance over the standard H.26L encoder during error-free transmission, while simultaneously allowing error recovery in the event of errors. Using turbo codes as coset codes, the performance of the proposed codec is evaluated and the efficacy of the proposed framework is demonstrated. The performance of the proposed approach can only improve with the use of superior coset codes.
In this paper, a new fuzzy logic-based lossy predictive coding system for gray-scale still image compression is developed. The proposed coder employs a recently introduced adaptive fuzzy prediction methodology in the ...
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In this paper, a new fuzzy logic-based lossy predictive coding system for gray-scale still image compression is developed. The proposed coder employs a recently introduced adaptive fuzzy prediction methodology in the predictor design. In addition, it adopts a novel fuzzy gradient-adaptive quantization scheme. The proposed coding technique possesses superior performance over its non-fuzzy counterparts especially at low bit quantization. This is due to the inherent adaptivity in the fuzzy prediction methodology as well as the gradient-adaptive quantization scheme. Simulation results are provided to demonstrate the efficient performance of the proposed fuzzy predictive coding system.
We present an architecture called the modular neural predictive coding architecture (Modular NPC). The Modular NPC is used for discriminative feature extraction (DFE). It provides an architecture based on phonetics kn...
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We present an architecture called the modular neural predictive coding architecture (Modular NPC). The Modular NPC is used for discriminative feature extraction (DFE). It provides an architecture based on phonetics knowledge applied to phoneme recognition. The phonemes are extracted from the Darpa-Timit speech database. Comparisons with coding methods (LPC, MFCC, PLP) are presented: they put in obviousness an improvement of the recognition rates.
In this paper a new adaptive fuzzy predictive coding system is introduced. The proposed coder employs the adaptive fuzzy prediction methodology developed in [Tian-Hu Yu, 1998]. This results in better prediction of smo...
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In this paper a new adaptive fuzzy predictive coding system is introduced. The proposed coder employs the adaptive fuzzy prediction methodology developed in [Tian-Hu Yu, 1998]. This results in better prediction of smooth as well as edge regions. In addition, the proposed coder adopts a novel fuzzy gradient-adaptive quantization scheme that switches between three well-designed nonuniform quantizers depending on the local gradient of the pixel to be coded. This, in turn, leads to reduced quantization errors in both smooth and edge regions and consequently higher perceptual quality of reconstructed images is achieved.
The coding of multimedia data streams is a vital factor in how well, and to what extent a given network can support popular applications. coding of speech and video, the two significant categories of data under consid...
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The coding of multimedia data streams is a vital factor in how well, and to what extent a given network can support popular applications. coding of speech and video, the two significant categories of data under consideration, currently follows the standards based on linear prediction, and MPEG respectively. The work documented in this paper is directed towards the development of an algorithm which permits coding of both voice and video data in an integrated manner with the same technique being used as the base for both the speech and the video coding algorithm of the system. The basic technique adopted for this integrated standard is M-ary predictive coding (MPC) [M. Vandana, January 2003]. MPC is a nonlinear model-based coding scheme which has currently been implemented successfully for speech coding [M. Vandana, January 2003]. The work documented in this paper involved the successful development and implementation of an MPC-based video coding algorithm.
In this paper a new fuzzy logic-based lossy predictive coding system for gray-scale still image compression is developed. The proposed coder employs a recently introduced adaptive fuzzy prediction methodology in the p...
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In this paper a new fuzzy logic-based lossy predictive coding system for gray-scale still image compression is developed. The proposed coder employs a recently introduced adaptive fuzzy prediction methodology in the predictor design. In addition, it adopts a novel fuzzy gradient-adaptive quantization scheme. The proposed coding technique possesses superior performance over its non-fuzzy linear counterparts especially at low bit quantization. This is due to the inherent adaptivity in the fuzzy prediction methodology as well as the gradient-adaptive quantization scheme. Simulation results are provided to demonstrate the efficient performance of the proposed fuzzy predictive coding system.
Speech feature extraction is one of the most important stage in the speech recognition process. In this paper, we propose a new neural networks architecture called the cooperative modular neural predictive coding (CMN...
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ISBN:
(纸本)0780381777
Speech feature extraction is one of the most important stage in the speech recognition process. In this paper, we propose a new neural networks architecture called the cooperative modular neural predictive coding (CMNPC). It is based on the interaction of discriminant experts DFE-NPC (discriminant feature extraction) optimized for macro-classification by the help of a criterion: the modelisation error ratio (MER). We propose a theoretical validation of this model by linking The MER with a likelihood ratio. The performances of this architecture are estimated in a phoneme recognition task. The phonemes are extracted from the Darpa-Timit speech database. Comparisons with coding methods (LPC, MFCC, PLP) are presented. They put in obviousness an improvement of the recognition rates.
Video transmission over the wireless or wired network require protection from channel errors since compressed video streams are very sensitive to transmission errors because of the use of predictive coding and variabl...
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Video transmission over the wireless or wired network require protection from channel errors since compressed video streams are very sensitive to transmission errors because of the use of predictive coding and variable length coding. In this paper, we propose a method to achieve robustness to transmission errors to the compressed bit-stream of wavelet based open source video codec, Dirac. By partitioning the wavelet transform coefficients into groups and independently processing each group using arithmetic and turbo coding, we could achieve the robustness to transmission errors of the compressed video stream in the packet erasure wired network. Simulation results show that the proposed technique can achieve up to 5dB PSNR gain over the un-partitioning method
In this paper, we propose a new predictive coding scheme for color data of three-dimensional (3-D) mesh models. We exploit connectivity and geometry information to improve coding efficiency. After ordering all vertice...
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In this paper, we propose a new predictive coding scheme for color data of three-dimensional (3-D) mesh models. We exploit connectivity and geometry information to improve coding efficiency. After ordering all vertices in a 3-D mesh model with a connectivity coding technique, we propose a geometry predictor to compress the color data efficiently. The predicted color can be obtained by a weighted sum of reconstructed colors for adjacent vertices using both angles and distances between the current vertex and adjacent vertices. Simulation results show that the proposed scheme provides enhanced coding efficiency over previous works for various 3-D mesh models
This paper presents a scalable fast mode decision algorithm to effectively choose the best coding mode among the 7 macroblock (MB) coding modes that could be used in H.264 video coding. The spatial and temporal comple...
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This paper presents a scalable fast mode decision algorithm to effectively choose the best coding mode among the 7 macroblock (MB) coding modes that could be used in H.264 video coding. The spatial and temporal complexity of the MB is analyzed to determine the order of searching in the mode decision priority queue such that the most probable mode will be checked first, followed by the second most probable mode, and so on. This process will be terminated as soon as the computed rate-distortion (RD) cost is below a threshold which is dependent on the complexity ratio of the current MR By adjusting the threshold we can choose a preferred tradeoff between timesaving and quality compromise. Experimental results show that the proposed fast mode decision algorithm can drastically reduce the encoding time up to 50% with negligible loss of coding efficiency.
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