High Quality Color Image Compression for Discrete Transform Domain Downward Conversion Block Based Image Coding
Deepak Kumar Gupta1, Neetesh Kumar Gupta2
1Deepak Kumar Gupta, Scholar, Department of Computer Science and Engineering, Technocrats Institute of Technology and Science, Bhopal (M. P.), India.
2Dr. Neetesh Kumar Gupta, Scholar, Department of Computer Science and Engineering, Technocrats Institute of Technology and Science, Bhopal (M. P.), India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1927-1932 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7131118419/2019©BEIESP | DOI: 10.35940/ijrte.D7131.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Text and image data are important elements for information processing almost in all the computer applications. Uncompressed image or text data require high transmission bandwidth and significant storage capacity. Designing and compression scheme is more critical with the recent growth of computer applications. Among the various spatial domain image compression techniques, multi-level Block partition Coding (ML-BTC) is one of the best methods which has the least computational complexity. The parameters such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are measured and it is found that the implemented methods of BTC are superior to the traditional BTC. This paves the way for a nearly error free and compressed transmission of the images through the communication channel.
Keywords: Multi-level Block Truncation Code (ML-BTC), Bit Map, Multi-level Quantization (MLQ), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE).
Scope of the Article: Image Analysis and Processing.