Open AccessDESIGN ANALYSIS OF 2-D DWT BASED IMAGE COMPRESSION USING FPGA FOR MEMORY SPEED OPTIMIZER.
Author(s) -
Vikash Kumar,
D. P. Gangwar,
Amit Kumar,
Monika Gupta,
M. Tech Scholar
Publication year - 2020
Publication title -
international journal of engineering, sciences and research technology
Language(s) - English
Resource type - Journals
ISSN - 2277-9655
DOI - 10.29121/ijesrt.v9.i7.2020.22
Subject(s) - discrete wavelet transform , computer science , image compression , data compression , throughput , filter (signal processing) , discrete cosine transform , algorithm , second generation wavelet transform , lossless compression , computer hardware , wavelet , wavelet transform , artificial intelligence , image processing , computer vision , image (mathematics) , telecommunications , wireless
Wavelet Transform is successfully applied a number of fields, covering anything from pure mathematics to applied science. Numerous studies, done on wavelet Transform, have proven its advantages in image processing and data compression and have made it a encoding technique in recent data compression standards along with multi- resolution decomposition of signal and image processing applications. Pure software implementations for the Discrete Wavelet Transform (DWT), however, seem the performance bottleneck in realtime systems in terms of performance. Therefore, hardware acceleration for the DWT has developed into topic of contemporary research. On the compression of image using 2-Dimensional DWT (2D-DWT) two filters are widely-used, a highpass as well as a lowpass filter. Because filter coefficients are irrational numbers, it's advocated that they must be approximated with the use of binary fractions. The truth and efficiency with that your filter coefficients are rationalized within the implementation impacts the compression and critical hardware properties just like throughput and power consumption. An expensive precision representation ensures good compression performance, but at the expense of increased hardware resources and processing time. Conversely, lower precision with the filter coefficients ends up with smaller, faster hardware, but at the expense of poor compression performance.