Survey Paper on Fractal Image Compression using Block Truncation Coding Technique
Author(s) -
Anshu Agrawal,
Pushpraj Singh
Publication year - 2018
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2018917486
Subject(s) - computer science , fractal compression , block truncation coding , coding (social sciences) , truncation (statistics) , image compression , fractal , block (permutation group theory) , data compression , image (mathematics) , algorithm , artificial intelligence , image processing , statistics , machine learning , mathematics , mathematical analysis , geometry
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 an efficient compression scheme is more critical with the recent growth of computer applications.Modern applications, in addition to high compression ratio, also demand for efficient encoding and decoding processes, so that computational constraint of many real-time applications is satisfied.Two generally utilized spatial area pressure strategies are block truncation coding (BTC) and vector quantization (VQ). BTC strategy brings about great quality picture with high piece rate, while the VQ is notable for low piece rate yet creates low quality pictures.In further work of this paper is multi-level BTC includes BTC algorithm as well as vector quantization method for purpose of multi-leveltechnique for gray and color image.
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