block truncation coding (ETC) is a simple and fast image compression algorithm which achieves constant bit rate of 2.0 bits per pixel, The method is however suboptimal. In the present paper we propose a modification o...
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block truncation coding (ETC) is a simple and fast image compression algorithm which achieves constant bit rate of 2.0 bits per pixel, The method is however suboptimal. In the present paper we propose a modification of ETC in which the compression ratio will be improved by coding the quantization data and the bit plane by arithmetic coding with an adaptive modelling scheme. The results compare favorable with other ETC variants. The bit rate for test image Lena is 1.53 bits per pixel with the mean square error of 16.51.
A new approach to improving block truncation coding for gray-scale image compression is proposed. A set of line and edge bit planes is defined independently of input images and adaptively selected to yield lower bit r...
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A new approach to improving block truncation coding for gray-scale image compression is proposed. A set of line and edge bit planes is defined independently of input images and adaptively selected to yield lower bit rates and better reconstructed image quality.
Many different versions of the block truncation coding image compression algorithm exist. In this paper we compare the different versions from the viewpoint of reconstructed image quality and computational complexity.
Many different versions of the block truncation coding image compression algorithm exist. In this paper we compare the different versions from the viewpoint of reconstructed image quality and computational complexity.
For a high-quality real-time image compression at moderate bit-rates the equi-spaced 3-level block truncation coding algorithms is an attractive coding method. Unfortunately, the present-day equi-spaced 3-level algori...
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For a high-quality real-time image compression at moderate bit-rates the equi-spaced 3-level block truncation coding algorithms is an attractive coding method. Unfortunately, the present-day equi-spaced 3-level algorithms are not optimum (in the mean square error sense). In this paper we describe a new equi-spaced 3-level algorithm which is fast and has nearly optimum mean square error.
This paper deals with clock truncationcoding for gray-scale images. An image region is segmented into unequal-sized square blocks according to local luminance variations and luminance values in the block are approxim...
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This paper deals with clock truncationcoding for gray-scale images. An image region is segmented into unequal-sized square blocks according to local luminance variations and luminance values in the block are approximated by triangular plane patches constructed from the luminance values at the four vertices of the block. Computational complexities of the unequal-sized block segmentations are discussed. Segmentations are classified into three types: top-down splitting;bottom-up merging;and generalized split-and-merge method. It is derived from the theoretical model that initial block size in the split-and-merge method for a given image of size 256 x 256 is 17 x 17. Segmentation by accumulated square error is preferred to human visual sensitivity. Experimental results using a standard image have clarified that the segmentation by the accumulated square error improves the quality of approximated image to 1 to 2 dB compared with segmentation by the mean square error. Moreover, it is confirmed that the approximation by triangular plane patches is superior to JPEG for the compression of the images that have much flat areas.
This paper describes an encoding technique-multi-level block truncation coding-that preserves the spatial details in digital images while achieving a reasonable compression ratio. An adaptive quantizer-level allocatio...
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This paper describes an encoding technique-multi-level block truncation coding-that preserves the spatial details in digital images while achieving a reasonable compression ratio. An adaptive quantizer-level allocation scheme is introduced, which minimizes the maximum quantization error in each block and substantially reduces the computational complexity in the allocation of optimal quantization levels. A 3.2:1 compression can be achieved by the multilevel block truncation coding itself. The truncated, or requantized, data are further compressed in a second pass using combined techniques of predictive coding, entropy coding, and vector quantization. The second pass compression can be lossless or lossy. The total compression ratios are about 4.1:1 for lossless second-pass compression, and 6.2:1 for lossy second-pass compression. The subjective results of the coding algorithm are quite satisfactory, with no perceived visual degradation.
Zeng has described an interpolative two-level block truncation coding (BTC) image compression algorithm. The Letter extends the work of Zeng to a three-level interpolative BTC algorithm. Compared to Zeng, the new algo...
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Zeng has described an interpolative two-level block truncation coding (BTC) image compression algorithm. The Letter extends the work of Zeng to a three-level interpolative BTC algorithm. Compared to Zeng, the new algorithm gives reconstructed images with substantially higher image quality with no increase in bit rate.
In this paper, we present a new algorithm that utilizes mathematical morphology for pyramidal coding of color images. We obtain lossy color image compression by using block truncation coding at the pyramid levels to a...
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In this paper, we present a new algorithm that utilizes mathematical morphology for pyramidal coding of color images. We obtain lossy color image compression by using block truncation coding at the pyramid levels to attain reduced data rates. The pyramid approach is attractive due to low computational complexity, simple parallel implementation, and the ability to produce acceptable color images at moderate data rates. In many applications, the progressive transmission capability of the algorithm is very useful. We will show experimental results for color images at data rates of 1.89 bits/pixel.
The authors extend the analysis of the block truncation coding (BTC) algorithm using a Hopfield neural network (HNN). They show that its performance is suboptimum (in the mean square error sense) and that alternative ...
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The authors extend the analysis of the block truncation coding (BTC) algorithm using a Hopfield neural network (HNN). They show that its performance is suboptimum (in the mean square error sense) and that alternative (non-neural network) BTC algorithms are available with virtually the same performance.
block truncation coding (BTC) is a recent technique used in the coding of image data. In the letter a modified technique for BTC coding of image data is presented which is algorithmically simple and hence easy to impl...
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block truncation coding (BTC) is a recent technique used in the coding of image data. In the letter a modified technique for BTC coding of image data is presented which is algorithmically simple and hence easy to implement. This new technique uses only the first-order statistical information as `block overhead??. The new algorithm is shown to be optimum in the mean-square sense for a particular class of BTC algorithms. The letter presents the results of using the new algorithm for a typical image and compares the performance with that of the earlier algorithm for the same image.
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