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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Block Truncation Coding ： ウィキペディア英語版 Block Truncation Coding Block Truncation Coding, or BTC, is a type of lossy image compression technique for greyscale images. It divides the original images into blocks and then uses a quantiser to reduce the number of grey levels in each block whilst maintaining the same mean and standard deviation. It is an early predecessor of the popular hardware DXTC technique, although BTC compression method was first adapted to colour long before DXTC using a very similar approach called Color Cell Compression. BTC has also been adapted to video compression BTC was first proposed by E.J Delp and O.R. Mitchell at Purdue University. Another variation of BTC is Absolute Moment Block Truncation Coding or AMBTC, in which instead of using the standard deviation the first absolute moment is preserved along with the mean. AMBTC is computationally simpler than BTC and also typically results in a lower Mean Squared Error (MSE). AMBTC was proposed by Maximo Lema and Robert Mitchell. Using sub-blocks of 4x4 pixels gives a compression ratio of 4:1 assuming 8-bit integer values are used during transmission or storage. Larger blocks allow greater compression ("a" and "b" values spread over more pixels) however quality also reduces with the increase in block size due to the nature of the algorithm. The BTC algorithm was used for compressing Mars Pathfinder's rover images.〔(【引用サイトリンク】title=Rover Camera Instrument Description )〕 == Compression procedure == A pixel image is divided into blocks of typically 4x4 pixels. For each block the Mean and Standard Deviation of the pixel values are calculated; these statistics generally change from block to block. The pixel values selected for each reconstructed, or new, block are chosen so that each block of the BTC compressed image will have (approximately) the same mean and standard deviation as the corresponding block of the original image. A two level quantization on the block is where we gain the compression and is performed as follows: $y(i,j)\; =$ \begin 1, & x(i,j) > \bar x \\ 0, & x(i,j) \le \bar x \end Here $x(i,j)$ are pixel elements of the original block and $y(i,j)$ are elements of the compressed block. In words this can be explained as: If a pixel value is greater than the mean it is assigned the value "1", otherwise "0". Values equal to the mean can have either a "1" or a "0" depending on the preference of the person or organisation implementing the algorithm. This 16 bit block is stored or transmitted along with the values of Mean and Standard Deviation. Reconstruction is made with two values "a" and "b" which preserve the mean and the standard deviation. The values of "a" and "b" can be computed as follows: $a=\backslash bar\; x\; -\; \backslash sigma\; \backslash sqrt\}$ $b=\backslash bar\; x\; +\; \backslash sigma\; \backslash sqrt\}$ Where $\backslash sigma$ is the standard deviation, m is the total number of pixels in the block and q is the number of pixels greater than the mean ($\backslash bar\; x$) To reconstruct the image, or create its approximation, elements assigned a 0 are replaced with the "a" value and elements assigned a 1 are replaced with the "b" value. $x(i,j)\; =$ \begin a, & y(i,j) = 0 \\ b, & y(i,j) = 1 \end This demonstrates that the algorithm is asymmetric in that the encoder has much more work to do than the decoder. This is because the decoder is simply replacing 1's and 0's with the estimated value whereas the encoder is also required to calculate the mean, standard deviation and the two values to use.〔Leis, J 2008, ELE4607 Advance Digital Communications, Module 3: Image & Video Coding. Lecture Slides, University of Southern Queensland, 2008.〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Block Truncation Coding」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.