An Innovative Lossless Compression Method for Discrete-Color Images

2015 ◽  
Vol 24 (1) ◽  
pp. 44-56 ◽  
Author(s):  
Saif Alzahir ◽  
Arber Borici
Keyword(s):  
Lossless Compression ◽  
Color Images ◽  
Compression Method

A new lossless compression method using Direction Adaptive-Discrete wavelet transform and Modified SPIHT coding

2021 ◽  
Author(s):  
N. Karthika Devi ◽  
G. Mahendran ◽  
S. Murugeswari ◽  
S. Praveen Samuel Washburn ◽  
D. Archana Devi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Lossless Compression ◽  
Discrete Wavelet ◽  
Compression Method ◽  
Spiht Coding

A Near-lossless Compression Method Based on CCSDS for Satellite Images

2012 ◽  
Vol 4 (18) ◽  
pp. 226-235
Author(s):  
ShenChuan Tai ◽  
TseMing Kuo ◽  
ChengHan Ho ◽  
TzuWen Liao
Keyword(s):  
Satellite Images ◽  
Lossless Compression ◽  
Compression Method

scholarly journals Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1062 ◽  
Author(s):  
Yuhang Dong ◽  
W. David Pan ◽  
Dongsheng Wu
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Red Blood Cell ◽  
Malaria Infection ◽  
Public Health Problem ◽  
Lossless Compression ◽  
Compression Method ◽  
Misclassification Rates ◽  
The Impact ◽  
Image Datasets

Malaria is a severe public health problem worldwide, with some developing countries being most affected. Reliable remote diagnosis of malaria infection will benefit from efficient compression of high-resolution microscopic images. This paper addresses a lossless compression of malaria-infected red blood cell images using deep learning. Specifically, we investigate a practical approach where images are first classified before being compressed using stacked autoencoders. We provide probabilistic analysis on the impact of misclassification rates on compression performance in terms of the information-theoretic measure of entropy. We then use malaria infection image datasets to evaluate the relations between misclassification rates and actually obtainable compressed bit rates using Golomb–Rice codes. Simulation results show that the joint pattern classification/compression method provides more efficient compression than several mainstream lossless compression techniques, such as JPEG2000, JPEG-LS, CALIC, and WebP, by exploiting common features extracted by deep learning on large datasets. This study provides new insight into the interplay between classification accuracy and compression bitrates. The proposed compression method can find useful telemedicine applications where efficient storage and rapid transfer of large image datasets is desirable.

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