Texture segmentation-based image coder incorporating properties of the human visual system
J. Jang,S.A. Rajala +1 more
- pp 2753-2756
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TLDR
This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image into texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS.Abstract:
A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image into texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes: perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. Good quality reconstructed images are obtained with 0.08 to 0.3 b/p for three different types of imagery. >read more
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References
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Journal ArticleDOI
Fractal-Based Description of Natural Scenes
TL;DR: The3-D fractal model provides a characterization of 3-D surfaces and their images for which the appropriateness of the model is verifiable and this characterization is stable over transformations of scale and linear transforms of intensity.
Journal Article
Run-length encodings.
TL;DR: Run-length encodings are used for determining explicit form of Huffman coding when applied to geometric distribution in the context of discrete geometry.
Journal ArticleDOI
Second-generation image-coding techniques
TL;DR: A new class of coding methods capable of achieving compression ratios as high as 70:1 is called second generation, which can be formed in this class: methods using local operators and combining their output in a suitable way and methods using contour-texture descriptions.
Journal ArticleDOI
Generalized kraft inequality and arithmetic coding
TL;DR: This coding technique requires no blocking, and the per-symbol length of the encoded string approaches the associated entropy within ∈, which is comparable to that of conventional coding methods.
Journal ArticleDOI
Segmented-image coding: performance comparison with the discrete cosine transform
TL;DR: In this paper, an efficient boundary following algorithm for coding segmented images is presented, which has the objective of minimising the sum squared error (SSE) of the representation.