Error-free image compression algorithm using classifying-sequencing techniques
TL;DR: The development of a new error-free digital image compression algorithm that achieves average bits-per-word ratios near the entropy of the neighboring pixel differences is discussed.
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Abstract: The development of a new error-free digital image compression algorithm is discussed. Without the help of any statistics information of the images being processed, this algorithm achieves average bits-per-word ratios near the entropy of the neighboring pixel differences. Because this algorithm does not involve statistical modeling, generation of a code book, or long integer–floating point arithmetics, it is simpler and, therefore, faster than the studied statistics codes, such as the Huffman code or the arithmetic code.
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Optical image compression and encryption methods
Ayman Alfalou,Christian Brosseau +1 more
TL;DR: Optical processing methodologies, based on filtering, are described that are applicable to transmission and/or data storage and the advantages and limitations of a set of optical compression and encryption methods are discussed.
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References
A universal algorithm for sequential data compression
Jacob Ziv,A. Lempel +1 more
TL;DR: The compression ratio achieved by the proposed universal code uniformly approaches the lower bounds on the compression ratios attainable by block-to-variable codes and variable- to-block codes designed to match a completely specified source.
Arithmetic coding for data compression
TL;DR: The state of the art in data compression is arithmetic coding, not the better-known Huffman method, which gives greater compression, is faster for adaptive models, and clearly separates the model from the channel encoding.
A Technique for High-Performance Data Compression
TL;DR: A new compression algorithm is introduced that is based on principles not found in existing commercial methods in that it dynamically adapts to the redundancy characteristics of the data being compressed, and serves to illustrate system problems inherent in using any compression scheme.
2.6K
Subjectively Optimal Quantization of Pictures
TL;DR: The results of experiments on gray scales and on quantized pictures are presented which lead to the choice of a compressor with a subjectively optimal parameter in the class of logarithmic compressors.
39
•Journal Article
Picture Coding
TL;DR: Good picture quality is obtained by coding the luminance and chrominance signals of color TV separately with DPCM and switched quantization into a 34 Mbit/s signal.
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