Performance analysis of low power vlsi techniques for image compression applications

Abstract

Image compression is a key technology in transmission and storage of digital images because of vast data associated with them. To minimize the storage space and for the fast transfer of the digital images, it is necessary for the images to undergo image compression. There are various techniques in which the images are being diagnosed, based on that the image compression is being performed. The choice of the filters in the image compression is an issue, which affects the quality of the image. An algorithmic concept of encoding information is given by wavelets in a manner that is layered according to level of detail. The layering approach will give approximations at various intermediate stages and the approximations can be saved and stored using less memory space than the original information. We discuss about 1. Improving performance of DWT by transmitting data and compressing it using a lifting based DWT algorithm in which we reduce multiplication blocks to show increase in speed at the channel and minimize power consumption of overall transmitted data. 2. Uses modified DA and optimized lifting-based scheme, and architectures are modeled using digital systems, which are used for studying different performance on compressed image data. 3. A novel architecture is developed by combining both wavelets and extended Kalman filter. Compression is done using Discrete Wavelet Transform and Nonlinearities are removed by Extended Kalman Filter during transmission. A Kalman filter is the process of finding the best estimate from noisy data amounts to filtering out the noise; DWT provides high compression ratios with no appreciable degradation of image quality. 4. Multidimensional 2D DWT and DTCWT which has shift invariance and Efficient Neural network algorithms are modeled to improve the compression, different performance analysis is studied.