For flexible speech coding, a Karhunen-Loeve Transform (KLT) based adaptive entropy-constrained quantization (KLT-AECQ) method is proposed. It is composed of backward-adaptive linear predictive coding (LPC) estimation...
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For flexible speech coding, a Karhunen-Loeve Transform (KLT) based adaptive entropy-constrained quantization (KLT-AECQ) method is proposed. It is composed of backward-adaptive linear predictive coding (LPC) estimation, KLT estimation based on the time-varying LPC coefficients, scalar quantization of the speech signal in a KLT domain, and superframe-based universal arithmetic coding based on the estimated KLT statistics. To minimize the outliers both in rate and distortion, a new distortion criterion includes the penalty in the rate increase. Gain adaptive step size selection and bounded Gaussian source model also cooperate to increase the perceptual quality. KLT-AECQ does not require either any explicit codebook or a training step, thus KLT-AECQ can have an infinite number of rate-distortion operating points regardless of time-varying source statistics. For the speech signal, the conventional KLT-based classified vector quantization (KLT-CVQ) and the proposed KLT-AECQ yield signal-to-noise ratios of 17.86 and 26.22, respectively, at around 16 kbits/s. The perceptual evaluation of speech quality (PESQ) scores for each method are 3.87 and 4.04, respectively(1).
For efficient variable-rate speech coding, Karhunen-Loeve transform based adaptive entropy-constrained vector quantization (KLT-AECVQ) is proposed. The proposed method consists of backward-adaptive linear predictive c...
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For efficient variable-rate speech coding, Karhunen-Loeve transform based adaptive entropy-constrained vector quantization (KLT-AECVQ) is proposed. The proposed method consists of backward-adaptive linear predictive coding (LPC) analysis, KLT estimation based on LPC coefficients, and lattice vector quantization followed by Huffman coding according to KLT statistics. As different statistics in an original-signal domain can be mapped into identical statistics in a KLT domain, only a Jew classified Huffman codebooks are sufficient to represent KLT-domain source statistics. KLT-AECVQ with 32 Huffman codebooks has comparable rate-distortion performance with theoretically optimal AECVQ with infinite number of Huffman codebooks. KLT-AECVQ also produces superior perceptual quality to KLT-based classified vector quantization (KLT-CVQ) that yielded better quality than conventional code-excited linear predictive (CELP) codec. Under five-sample delay constraints, KLT-AECVQ has also three times, lower complexity than CELP codec(1).
A novel coding paradigm is proposed to jointly optimize the prediction, quantization, and entropy coding modules, thereby approaching optimality in scalable video coding. It departs from conventional video coding sche...
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ISBN:
(纸本)9781467325332;9781467325349
A novel coding paradigm is proposed to jointly optimize the prediction, quantization, and entropy coding modules, thereby approaching optimality in scalable video coding. It departs from conventional video coding schemes that consider prediction, transformation, quantization, and entropy coding, as largely separate sequential functional components. The method draws inspiration from an early estimation-theoretic approach, developed by our group for enhancement layer prediction, which efficiently combines all the information available to the enhancement layer coder, to produce the optimal prediction. The framework is significantly expanded here to also incorporate optimization of entropy-constrained quantization and arithmetic coding, while fully accounting for hitherto ignored relevant factors, inherent to predictive scalable coding, including information from the base layer quantization operation, and from the enhancement layer motion compensated reference. Experimental evidence is provided for substantial coding gains over conventional scalable video coding.
We investigate the theoretical and practical rate-distortion (R-D) performance of resolution-constrainedquantization (RCQ) combined with lossless coding (RCQ+). Based on the high-rate theory, the required rate differ...
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We investigate the theoretical and practical rate-distortion (R-D) performance of resolution-constrainedquantization (RCQ) combined with lossless coding (RCQ+). Based on the high-rate theory, the required rate difference between RCQ and RCQ+, at a given mean distortion, is found to be the Kullback-Leibler distance (KLD) between the source probability density function (PDF) and its rateless centroid density function. Thus, the rate reduction in RCQ+ is diminished as vector dimensionality increases or as the source PDF approaches uniform density. In the experiments with Gaussian data, the R-D performance of high-rate derivation is verified. Huffman coding is implemented on top of the conventional RCQ methods such as ITU-T G. 711 and G.722.2 speech coders, and the rate reduction with RCQ+ is found to be decreased for lower values of the KLD.
In this paper, we establish a probabilistic framework for adaptive transform coding that leads to a generalized Lloyd type algorithm for transform coder design. Transform coders are often constructed by concatenating ...
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In this paper, we establish a probabilistic framework for adaptive transform coding that leads to a generalized Lloyd type algorithm for transform coder design. Transform coders are often constructed by concatenating an ad hoc choice of transform with suboptimal bit allocation and quantizer design. Instead, we start from a probabilistic latent variable model in the form of a mixture of constrained Gaussian mixtures. From this model, we derive an transform coder design algorithm, which integrates optimization of all transform coder parameters. An essential part this algorithm is our introduction of a new transform basis-the coding optimal transform-which, unlike commonly used transforms, minimizes compression distortion. Adaptive transform coders can be effective for compressing databases of related imagery since the high overhead associated with these coders can be amortized over the entire database. For this work, we performed compression experiments on a database of synthetic aperture radar images. Our results show that adaptive coders improve compressed signal-to-noise ratio (SNR) by approximately 0.5 dB compared with global coders. Coders that incorporated the coding optimal transform had the best SNRs on the images used to develop the coder. However, coders that incorporated the discrete cosine transform generalized better to new images.
In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary p...
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In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for, this signal. We show that, due to the statistical properties of the SAR signa, 1, an along-range linear predictor With few, taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs. quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods. based on transform-coding;fractional output bit-rates can, also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.
We develop an iterative, hillclimbing-based assignment algorithm for the approximate solution of discrete-parameter cost minimization problems defined on the pixel sites of an image. While the method is applicable to ...
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We develop an iterative, hillclimbing-based assignment algorithm for the approximate solution of discrete-parameter cost minimization problems defined on the pixel sites of an image. While the method is applicable to a number of problems including encoding, decoding, and segmentation, this letter focuses on entropy-constrained encoding. For typical statistical image models, the globally optimal solution requires an intractable exhaustive search, while standard greedy methods, though tractable in computation, may be quite suboptimal. Alternatively, our method is guaranteed to perform no worse (and typically performs significantly better) than greedy encoding, yet with manageable increases in complexity. The new approach uses dynamic programming as a local optimization "step," repeatedly applied to the rows (or columns) of the image, until convergence. For a DCT framework, with entropy-constrained TCQ applied to the coefficient sources, the new method gains as much as 0.8 dB over standard greedy encoding.
In this paper we propose to employ entropy-constrained predictive coding for lossy compression of SAR raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process i...
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ISBN:
(纸本)0819445606
In this paper we propose to employ entropy-constrained predictive coding for lossy compression of SAR raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. The proposed predictive coding algorithm performs entropy-constrained quantization of the prediction error, followed by entropy coding;the algorithm exhibits a number of advantages, and notably a very high performance gain, with respect to other techniques such as FBAQ or methods based on transform coding. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm significantly outperforms FBAQ as to SNR, at a computational cost compatible with modem SAR systems.
Compression of SAR imagery for battlefield digitization is discussed in this paper. The images are first processed to separate out possible target areas. These target areas are compressed losslessly to avoid any degra...
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ISBN:
(纸本)0819424358
Compression of SAR imagery for battlefield digitization is discussed in this paper. The images are first processed to separate out possible target areas. These target areas are compressed losslessly to avoid any degradation of the images. The background information which is usually necessary to establish context, is compressed using a hybrid vector quantization algorithm. An adaptive variable rate residual vector quantizer is used to compress the residual signal generated by a neural network predictor. The vector quantizer codebooks are optimized for entropy coding using an entropy-constrained algorithm to further improve the coding performance. This constrained vector-quantizer combination performs extremely well as suggested by the experimental results.
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