scholarly journals Universality of Logarithmic Loss in Successive Refinement

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 158 ◽  
Author(s):  
Albert No
Keyword(s):  
Low Complexity ◽  
Lossy Compression ◽  
Compression Algorithm ◽  
Universal Property ◽  
Successive Refinement ◽  
Logarithmic Loss

We establish an universal property of logarithmic loss in the successive refinement problem. If the first decoder operates under logarithmic loss, we show that any discrete memoryless source is successively refinable under an arbitrary distortion criterion for the second decoder. Based on this result, we propose a low-complexity lossy compression algorithm for any discrete memoryless source.

Implementation of LOCO-I Lossless Image Compression Algorithm for Deep Space Applications

2014 ◽  
Vol 3 (3) ◽  
pp. 98
Author(s):  
P. Praveena
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Low Complexity ◽  
Vital Role ◽  
Space Science ◽  
Compression Algorithm ◽  
Deep Space ◽  
Space Applications ◽  
Lossless Image Compression ◽  
Image Compression Algorithm

<p>Present emerging trend in space science applications is to explore and utilize the deep space. Image coding in deep space communications play vital role in deep space missions. Lossless image compression has been recommended for space science exploration missions to retain the quality of image. On-board memory and bandwidth requirement is reduced by image compression. Programmable logic like field programmable gate array (FPGA) offers an attractive solution for performance and flexibility required by real time image compression algorithms. The powerful feature of FPGA is parallel processing which allows the data to process quicker than microprocessor implementation. This paper elaborates on implementing low complexity lossless image compression algorithm coder on FPGA with minimum utilization of onboard resources for deep space applications.</p>

scholarly journals Context Based Predictive Information

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 645
Author(s):  
Yuval Shalev ◽  
Irad Ben-Gal
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Lossy Compression ◽  
Compression Algorithm ◽  
Predictive Information ◽  
Real Dataset ◽  
Information Bottleneck ◽  
The U.S

We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. The advantage of this approach over existing methods resides in the case of sparse predictive information (SPI) conditions, where the ratio between the number of informative sequences to uninformative sequences is small. It is shown that the CBPI achieves a better PI estimation than benchmark methods by ignoring uninformative sequences while improving explainability by identifying the informative sequences. We also provide an implementation of the CBPI algorithm on a real dataset of large banks’ stock prices in the U.S. In the last part of this paper, we show how the CBPI algorithm is related to the well-known information bottleneck in its deterministic version.

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