Development of High Quality Color Image Compression using Block Transformation
Prashant Parashar1, Divakar Singh2

1Prashant Parashar, Department of Computer Science and Engineering, Name of the Affiliated College or University/Industry, City, Country.
2Dr. Divakar Singh, Department of Computer Science and Engineering, Assistant Professor in BUIT, Barkatullah University, Bhopal (M.P)., India.

Manuscript received on 05 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2566-2572 | Volume-8 Issue-10, August 2019 | Retrieval Number: J91870881019/19©BEIESP | DOI: 10.35940/ijitee.J9187.0881019
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Abstract: The upcoming era of social media will be highly equipped with the images and the videos. Images or multimedia content sharing and storing services are still costly for common man which is need to be resolved to cover all the users either middle class or high society users. Various online platforms have filled the gaps for freedom of expression for everyone. The huge demand of the multimedia data sharing through the telecommunication networks has increased. The compression of images has changed the requirements for effective transmission and storage media. With the convenience of accessibility of press tools and digital image web exchange, there has been a dramatic increase. Image is the least component of multimedia information and includes a important portion of the velocity of communication for multimedia data communication n Developments in image compression techniques have therefore developed potential requirement. For all pictures, a fundamental concept of image formation is that the pixels are linked and comprise extremely useless data afterwards. The primary objective of this job is to discover in the image reduced associated pixel intensities. In this work an adaptive frequency domain block processing for color image compression has developed and simulated.
Keywords: Image Compression, Color Image, PSNR, Lossy Compression, Block Transform.

Scope of the Article: Image Processing and Pattern Recognition