Ferguson, K.L. and Allinson, N.M. (1999) Rate constrained selforganzing neural maps and efficient psychovisual methods for low bit rate video coding. In: Neural Networks for Signal Processing IX, 1999, 2325 August 1999, Madison, WI, USA.
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Item Type:  Conference or Workshop contribution (Paper) 

Item Status:  Live Archive 
Abstract
The video coding problem is essentially an operational distortionrate issue where the underlying input pixel data, probability distributions and dimensions are discrete, unknown and not smooth. In the low bit rate case the high resolution assumptions for vector quantization are not strictly valid and the problem is exacerbated. However, by considering the rateconstrained operational points on sets of selforganizing neural maps (SOMs), provides a methodology for selecting locally optimal vector quantizers. The learning process of the standard SOM algorithm is modified to minimize the distortion subject to a constraint of entropy approximation. The applied training set is adapted to suit the proposed coding environment. Operating in the discrete wavelet transform (DWT) domain is well suited to the inclusion of a psychovisual model. The spatial frequency response, the multiresolution scene analysis and the central focusing aspects of the visual cortex are incorporated into the model. The resulting video coding algorithm is bit rate scalable from 10 k bits per second (bits/s) and provides subjectively acceptable video at a fixed frame rate or 10 frames per second (f.p.s.) with a QCIF pixel resolution
Additional Information:  The video coding problem is essentially an operational distortionrate issue where the underlying input pixel data, probability distributions and dimensions are discrete, unknown and not smooth. In the low bit rate case the high resolution assumptions for vector quantization are not strictly valid and the problem is exacerbated. However, by considering the rateconstrained operational points on sets of selforganizing neural maps (SOMs), provides a methodology for selecting locally optimal vector quantizers. The learning process of the standard SOM algorithm is modified to minimize the distortion subject to a constraint of entropy approximation. The applied training set is adapted to suit the proposed coding environment. Operating in the discrete wavelet transform (DWT) domain is well suited to the inclusion of a psychovisual model. The spatial frequency response, the multiresolution scene analysis and the central focusing aspects of the visual cortex are incorporated into the model. The resulting video coding algorithm is bit rate scalable from 10 k bits per second (bits/s) and provides subjectively acceptable video at a fixed frame rate or 10 frames per second (f.p.s.) with a QCIF pixel resolution 

Keywords:  discrete wavelet transforms, entropy, frequency response, image resolution, probability, rate distortion theory, selforganising feature maps, teleconferencing, transform coding, vector quantisation, video coding, visual perception 
Subjects:  G Mathematical and Computer Sciences > G730 Neural Computing 
Divisions:  College of Science > School of Computer Science 
ID Code:  5128 
Deposited On:  02 May 2012 06:23 
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