Asymptotically efficient quantizing

Gish, H.; Pierce, John R.

doi:10.1109/tit.1968.1054193

Cited by 429 publications

(234 citation statements)

References 5 publications

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“…14) is quantized using a uniform rounding function and an adaptively tuned step-size ∆ W [r]. The output entropy of a uniform quantizer is asymptotically smaller than that of any other quantizer, independent of the density function of the error criteria according to Gish and Pierce [22]. Quantization is performed element-wise.…”

Section: Randomized Quantization Functionsmentioning

confidence: 99%

Learning convolutive features for storage and transmission between networked sensors

Fréin

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Section: Randomized Quantization Functionsmentioning

confidence: 99%

Learning convolutive features for storage and transmission between networked sensors

Fréin

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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“…where Q is the quantization step [31]. Substituting (3) into (2), a rate-quantization (R-Q) model can be formulated by neglecting the high-order terms.…”

Section: B Estimations Of Spatial and Temporal Complexitiesmentioning

confidence: 99%

No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

Yang

Wan

Xie

et al. 2010

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.Index Terms-Coding distortion, networked video, noreference video quality assessment, packet loss, temporal pooling.

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“…Specifically, if the density of a scalar random variable is reasonably smooth, then at sufficiently high rate the distribution within a quantization interval is uniform. It is well known that uniform quantizers are asymptotically (at high resolution) optimal for variable rate coding, irrespective of the density of the source to be quantized [14]. Therefore, we use uniform quantizers throughout the paper.…”

Section: B High Rate Approximationsmentioning

confidence: 99%

A necessary and sufficient condition for transform optimality in source coding

Akyol

Rose

2011

2011 IEEE International Symposium on Information Theory Proceedings

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Abstract-It is well-known for transform coding of multivariate Gaussian sources, that the Karhunen Loeve transform (KLT) minimizes the mean square error distortion. However, finding the optimal transform for general non-Gaussian sources has been an open problem for decades, despite several important advances that provide some partial answers regarding KLT optimality. In this paper, we present a necessary and sufficient condition for optimality of a transform when high resolution, variable rate quantizers are employed. We present not only a complete characterization of when KLT is optimal, but also a determining condition for optimality of a general (non-KLT) transform. This necessary and sufficient condition is shown to have direct connections to the well studied source separation problem. This observation can impact source separation itself, as illustrated with a new optimality result. Finally, we combine the transform optimality condition with algorithmic tools from source separation, to derive a practical numerical method to search for the optimal transform in source coding.

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Asymptotically efficient quantizing

Cited by 429 publications

References 5 publications

Learning convolutive features for storage and transmission between networked sensors

Learning convolutive features for storage and transmission between networked sensors

No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

A necessary and sufficient condition for transform optimality in source coding

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