This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a move-to-front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point...
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ISBN:
(纸本)9781479965946
This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a move-to-front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients;and then these resulting coefficients are converted using the move-to-front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods;using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and move-to-front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.
We present work-optimal PRAM algorithms for Burrows-Wheeler compression and decompression of strings over a constant alphabet. For a string of length n, the depth of the compression algorithm is O(log(2)n), and the de...
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We present work-optimal PRAM algorithms for Burrows-Wheeler compression and decompression of strings over a constant alphabet. For a string of length n, the depth of the compression algorithm is O(log(2)n), and the depth of the corresponding decompression algorithm is O(logn). These appear to be the first polylogarithmic-time work-optimal parallel algorithms for any standard lossless compression scheme. The algorithms for the individual stages of compression and decompression may also be of independent interest: (1) a novel O(logn)-time, O(n)-work PRAM algorithm for Huffman decoding;(2) original insights into the stages of the BW compression and decompression problems, bringing out parallelism that was not readily apparent, allowing them to be mapped to elementary parallel routines that have O(logn)-time, O(n)-work solutions, such as: (i) prefix-sums problems with an appropriately-defined associative binary operator for several stages, and (ii) list ranking for the final stage of decompression. Follow-up empirical work suggests potential for considerable practical speedups on a PRAM-driven many-core architecture, against a backdrop of negative contemporary results on common commercial platforms. (C) 2013 Elsevier B.V. All rights reserved.
In a color-mapped (pseudo-color) image, pixel values represent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with pseudo-...
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In a color-mapped (pseudo-color) image, pixel values represent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with pseudo-color images, while universal compressors, such as Gzip, Pkzip and Compress, yield better compression gain. Recently, Burrows and Wheeler introduced the Block Sorting Lossless Data Compression Algorithm (BWA). The BWA algorithm received considerable attention. It achieves compression rates as good as context-based methods, such as PPM, but at execution speeds closer to Ziv-Lempel techniques. The BWA algorithm is mainly composed of a block-sorting transformation which is known as Burrows-Wheeler Transformation (BWT), followed by move-To-front (MTF) coding. We introduce a new block transformation, Linear Order Transformation (LOT). We delineate its relationship to Burrows-Wheeler Transformation and show that LOT is faster than BWT transformation. We then show that when MTF coder is employed after the LOT, the compression gain obtained is better than the well-known compression techniques, such as GIF, JPEG, CALIC, Gzip, LZW (Unix Compress) and the BWA for pseudo-color images. (C) 1999 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(99)00506-1].
In a Psuedo-color (Color-mapped) image pixel values represent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with Psuedo-c...
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ISBN:
(纸本)0819429155
In a Psuedo-color (Color-mapped) image pixel values represent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with Psuedo-color images, while universal compressors, such as Gzip, Pkzip and Compress, yield better compression gain. Recently, Burrows and Wheeler [3] introduced the Block Sorting Lossless Data Compression Algorithm (BWA). The BWA algorithm received considerable attention. It achieves compression rates as good as context-based methods, such as PPM, but at execution speeds closer to Ziv-Lempel techniques [6]. The BWA algorithm is mainly composed of a block-sorting transformation which is known as Burrows-Wheeler Transformation (BWT), followed by move-To-front (MTF) coding. In this paper, we introduce a new block transformation, Linear Order Transformation (LOT). We delineate its relationship to Burrows-Wheeler Transformation (BWT) and show that LOT is faster than BWT transformation. We then show that when MTF coder is employed after the LOT, the compression gain obtained is better than the well-known compression techniques, such as GIF, JPEG, CALLIC, Gzip, LZW (Unix Compress) and the BWA for psuedo-color images.
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