Hybrid two-dimensional dual tree—biorthogonal wavelet transform and discrete wavelet transform with fuzzy inference filter for robust remote sensing image compression

Abstract

Image compression plays a crucial role in digital image processing, it is also very important for efficient transmission and storage of images. In particular, remote sensing makes it possible to collect image data on dangerous or inaccessible areas (in Roy et al. Signal Process 128: 262–273, 2016). The methods are introduced in previous research for the efficient image compression with less error rate. The existing method is named as 2D-dual tree-complex wavelet transform (2D-DT-CWT) with fuzzy inference filter (FIF) based image compression algorithm which is used for the aid of remote sensing image compression. However it has issue with time complexity and lack in robust compression ratios. To avoid the above mentioned issues, in the proposed system, the approach enhanced called as hybrid 2D-oriented biorthogonal wavelet transform (2D-BWT) by using Windowed all phase digital filter (WAPDF) based on discrete wavelet transform (DWT) for robust image compression algorithm. The proposed system contains modules such as image compression using 2D-DWT, 2D-BWT using WAPDF for improving transformation, coefficient selection using FIF. Then context-adaptive binary arithmetic coding (CABAC) with lattice vector quantization (LVQ) is proposed for encoding the wavelet significant coefficients. DWT is used to focus on the provision of high quality compression images and BWT is used to improve the transformation process. The experimental results show that hybrid-2D-BDWT can help in significant improvement of the transform coding gain, specifically for remote sensing images having good resolution. In this research, the comparison of the proposed work is done with the existing 2D-oriented wavelet transform (2D-OWT) and 2D-DT-CWT. Also, the new compression method is simple, and the memory requirement in the operation process is very less. It provides robust image compression ratio and high quality images using transformation methods.