HYBRID IMAGE COMPRESSION BASED ON FEATURES IMPORTANCE USING GAN

Abstract

Hybrid image compression techniques are the one to be used to compress and decompress the sensitive information without losing data, non-sensitive information with losing some information and completely removing the irrelevant information. Even though different works have been done to handle these complex compression tasks, there are still problems. Problem of field specific hybridization which means the information which is sensitive in health may not be sensitive in other areas while the information which is irrelevant in health can be sensitive or relevant information in other areas. Lack of the way to control percentage of information to be lost for a given image feature and the problem of using local features of an image rather than using global image features. To save the problem by using we proposed the hybrid model that contains 4 modules namely feature extraction module, lossless module, lossy module and combiner module. The feature extraction module takes an image and name of the feature to be extracted to extract the required feature and return it. The lossy module contains two submodules called lossy 75 and lossy 25 submodules. Lossy75 uses 0.5 bits to encode each pixel of a features. Lossy25 uses 0.25bits to encode each pixel of a feature. Only the features specified are going to be extracted and compressed from an image so that irrelevant features could be removed. The lossy submodules can take more than one feature at a time. The size of the data from both lossy submodules is dynamic. Two different features could be compressed using [0.50,0.25] bpp for each and average 0.373 bpp or [0.25,0.25] bpp and averge 0.25 bpp or [0.5,0.5] bpp or average 0.5 bpp. Generally, by this work we able to make hybrid image compression model flexible by which decision on feature importance can be specified by user. Also, we are the first to make lossy compression model of dynamic output and that does not depend on context of the feature. In the future we will work on the feature extraction module to add other features not included for this thesis to improve the model