VLSI Architectures for Image Interpolation: A Survey

Abstract

Image interpolation is a method of estimating the values at unknown points using the known data points. This procedure is used in expanding and contrasting digital images. In this survey, different types of interpolation algorithm and their hardware architecture have been analyzed and compared. They are bilinear, winscale, bi-cubic, linear convolution, extended linear, piecewise linear, adaptive bilinear, first order polynomial, and edge enhanced interpolation architectures. The algorithms are implemented for different types of field programmable gate array (FPGA) and/or by different types of complementary metal oxide semiconductor (CMOS) technologies like TSMC 0.18 and TSMC 0.13. These interpolation algorithms are compared based on different types of optimization such as gate count, frequency, power, and memory buffer. The goal of this work is to analyze the different very large scale integration (VLSI) parameters like area, speed, and power of various implementations for image interpolation. From the survey followed by analysis, it is observed that the performance of hardware architecture of image interpolation can be improved by minimising number of line buffer memory and removing superfluous arithmetic elements on generating weighting coefficient.