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Image Compression is a demanding field in this era of communication. There is a need to study and analyze the literature for image compression, as the demand for images, video sequences and computer animation has increased at very high rate so that the increment is drastically over the years. Multimedia data whether graphics, audio, video data which is uncompress requires considerable transmission bandwidth and storage capacity. So this leads to the need of compression of images and all multimedia applications to save storage and transmission time. In this study we discuss different compression algorithms used to reduce size of images without quality reduction.

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Image fusion is an approach which is used to amalgamate the corresponding features in a sequence of input images to a single composite image that preserves all the significant features of the input images. Image fusion is also known as pansharpening. It is a method which is used to integrate and add the geometric detail of a high-resolution panchromatic (Pan) image and the information of color of a low-resolution multispectral (MS) image for the production of a high-resolution MS image. This methodology is mainly most important and significant for any large-scale applications. Image fusion classification based on its systems (models and algorithms) are considered and overviewed in this survey. The basic two algorithms categories are analyzed and compared to each other, in the analysis and discussion section the major points to be considered while performing image fusion are highlighted.

Brain image compression is known as a subfield of Brain image compression. It allows the deep analysis and measurements of brain images in different modes. Brain images are compressed to analyze and diagnose in an effective manner while reducing the image storage space. This survey study describes the different existing techniques regarding brain image compression. The techniques come under different categories. The study also discusses the different categories.