In order to obtain homogeneous regions and smooth contours for region-oriented image compression, gradient-coupled spiking cortex model is designed and applied to digital image segmentation. Inspired by the knowledge ...
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In order to obtain homogeneous regions and smooth contours for region-oriented image compression, gradient-coupled spiking cortex model is designed and applied to digital image segmentation. Inspired by the knowledge of visual cortex, the model is composed of neurons with spike coupling and gradient enhancement, and it is same as the one in the visual cortex which can distinguish some objects in real scene through capturing boundary information. The model smoothes pixels within regions and enhances pixels at boundaries by creating a fitting function. Outputs of the model are the desired segmentedimage after connection components label. Experiments show that the method not only detects regions of original image, but also remains succinct effective contours, so it is suitable for region-oriented image compression. (c) 2012 Elsevier B.V. All rights reserved.
This paper experimentally compares several variants of segmented image coding (SIC), some of which are new. First it compares two methods for approximating the image intensity, with respect to quality of the reconstru...
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This paper experimentally compares several variants of segmented image coding (SIC), some of which are new. First it compares two methods for approximating the image intensity, with respect to quality of the reconstructed image, computational complexity and memory requirements. An extension of an existing segmentation algorithm (edgmentation) is introduced and compared with two other segmentation algorithms suitable for SIC. Finally, a hybrid scheme is proposed which utilizes both SIC and block transform coding (BTC) methods. The hybrid scheme has 35% less computational complexity than the full SIC approach, while it retains all the edge information produced by the segmentation algorithm. (C) 1997 Elsevier Science B.V.
This paper describes some new algorithms for segmented Still image and image Sequence coding. A new filter is introduced for reducing the number of contour points produced by segmentation, and also the number of bits ...
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This paper describes some new algorithms for segmented Still image and image Sequence coding. A new filter is introduced for reducing the number of contour points produced by segmentation, and also the number of bits needed to code them, leading to higher compression ratios without visible subjective degradation of the image quality. The impact of using such a contour points' reduction filter in image sequence: compression is studied. The inclusion of the filter in a new video codec scheme leads to higher frame rates for almost the: same subjective video quality, especially when coding complex scenes. A simple way to control the video bit-rate is also described. (C) 1999 Elsevier Science B.V. All rights reserved.
The paper first briefly discusses the advantages and disadvantages of using orthonormal (as opposed to non-orthogonal) bases in segmented image coding, and shows that the optimal choice is application-dependent. Next,...
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The paper first briefly discusses the advantages and disadvantages of using orthonormal (as opposed to non-orthogonal) bases in segmented image coding, and shows that the optimal choice is application-dependent. Next, it introduces fast algorithms for computing orthonormal base functions on an arbitrarily-shaped region. The algorithms are extensions of the 'natural' polynomial recursive orthogonalization (PRO) algorithm, introduced earlier by the author and differ from it in that they allow new orthogonalization orders and new types of base functions (cosines and warped polynomials in addition to ordinary polynomials). The algorithms are typically 1.5 to 3 times faster than the corresponding Gram-Schmidt (GS) methods. Three of the new algorithms, called RECT, TOTDIAG and XY are investigated in detail. The RECT and TOTDIAG algorithms are typically 15% to 30% slower than 'natural' PRO, but still 1.5 to 2.5 times faster than GS. Also, their computational advantage over GS increases;vith the number of computed base functions. A preliminary experiment shows that the combined use of the RECT or TOTDIAG base with the natural base in different areas of the image may lead to a better approximation performance, albeit at the expense of extra computations. (C) 1997 Elsevier Science B.V.
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