Adaptive feature-based motion analysis and its application to interframe coding

Contemporary methods of interframe coding use motion estimation and compensation as a means of reducing the prediction error in algorithms which provide a large reduction in data rate. These methods can broadly be divided into two categories, pel recursive and block matching algorithms. Further work...

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Bibliographic Details
Published in:Fifth International Conference on Image Processing and its Applications pp. 31 - 35
Main Authors: Garnham, N.W, Ibrahim, M.K
Format: Conference Proceeding
Language:English
Published: London IEE 1995
Subjects:
Codes
Optical information, image and video signal processing
Pattern recognition
low complexity algorithm
image classification
adaptive feature-based motion analysis
prediction error reduction
data rate reduction
block matching algorithms
image matching
image texture
motion compensation
pel recursive algorithms
full-search block matching method
motion search
adaptive signal processing
image segmentation
real-time processing
interframe coding
motion estimation
image displacement
audio-visual scene
model-based coding
image deformation
image coding
image resolution
low-resolution image
ISBN:0852966423, 9780852966426
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Summary:Contemporary methods of interframe coding use motion estimation and compensation as a means of reducing the prediction error in algorithms which provide a large reduction in data rate. These methods can broadly be divided into two categories, pel recursive and block matching algorithms. Further work has investigated model-based coding, using the displacement and deformation of image objects as the criteria for interframe coding. Consideration of a typical audio-visual scene indicates that a motion search of the entire image is unnecessary, since motion is mainly concentrated in the detailed area around the subject's face. The technique described can be considered as a step between the simple full-search block matching method and the more involved approach of model-based coding. It is demonstrated that, using a low-resolution image, objects can be segmented into features of consistent texture, small enough to provide interframe detail and large enough to provide the basis of an algorithm of low complexity and real-time processing potential.
ISBN:0852966423
9780852966426
DOI:10.1049/cp:19950614