BS Z 17:2000 pdf free.Space data and information transfersystems—Data systems —Lossless datacompression.
BS Z 17 This Recommendation defines for standardization a particular adaptive source coding algorithmthat has widespread applicability to many forms of digital data. In particular, the science datafrom many types of imaging or non-imaging instruments are well suited for the application ofthis algorithm.
There are two classes of source coding methods: Lossless and Lossy.
A Lossless source coding technique preserves source data accuracy and removes redundancy inthe data source. In the decoding process, the original data can be reconstructed from thecompressed data by restoring the removed redundancy; the decompression process adds nodistortion. This technique is particularly useful when data integrity cannot be compromised.The price it pays is generally a lower Compression Ratio, which is defined as the ratio of thenumber of original uncompressed bits to the number of compressed bits including overhead bitsnecessary for signaling parameters.
On the other hand, a Lossy source coding method removes some of the source informationcontent along with the redundancy. The original data cannot be fully restored and data distortionoccurs.However, if some distortion can be tolerated, Lossy source coding generally achieves ahigher compression ratio. By controlling the amount of acceptable distortion and compression.this technique may enable acquisition and dissemination of mission data within a critical timespan.
This Recommendation addresses only Lossless source coding and does not attempt to explain thetheory underlying the operation of the algorithm.
The Lossless source coder consists of two separate functional parts: the preprocessor and theadaptive entropy coder, as shown in figure 2-1.The preprocessor function is a reversible operation，and,in general, the best Losslesspreprocessor will meet the above conditions and produce the lowest entropy, which is a measureof the smallest average number of bits that can be used to represent each sample.
This Recommendation does not attempt to explain methods for choosing a preprocessing stage.This Recommendation does provide the definition of a basic preprocessing stage that may besuitable for many applications. However, it is important that the user carefully address this issuesince careful selection of an appropriate preprocessing stage is essential for efficient compressionand depends on the source-data characteristics. Interested users should refer to reference [B2].
Adaptive Entropy Coder:
The function of the Adaptive Entropy Coder is to calculate uniquely decipherable, variable-length codewords corresponding to each block of samples input from the preprocessor. Theentropy coder incorporates multiple coding options,each exhibiting efficient performance overdifferent yet overlapping ranges of entropy. The coder selects the coding option that gives thehighest compression ratio among the various options on the same block of J samples. A code-option ‘identifier’, requiring only a few bits, is attached before the first codeword bit in a codedblock to signal the coding option to the decoder for proper decompression. Since the block size Jis small (16 or fewer samples) and a new code option is selected for each block, the overallcoding can be adjusted to adapt to rapid changes in data statistics.BS Z 17 pdf download.