Hybrid two-dimensional dual tree—biorthogonal wavelet transform and discrete wavelet transform with fuzzy inference filter for robust remote sensing image compression S. Sudhakar Ilango 1 • V. Seenivasagam 2 • R. Madhumitha 1 Received: 12 November 2017 / Revised: 24 January 2018 / Accepted: 2 February 2018 Ó Springer Science+Business Media, LLC, part of Springer Nature 2018 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. Keywords Fuzzy inference filter  Biorthogonal wavelet transform  Discrete wavelet transform  Windowed all phase digital filter  Lattice vector quantization  Oriented wavelet transform 1 Introduction The research of image compression focuses at decreasing the number of bits required to represent an image by eliminating the spatial and spectral redundancies as possi- ble [1]. In the present days, the image compression method is most efficient for the applications like the transmission and the storage in the data bases [2]. Characteristics of lossy image compression model which holds the three directly associated elements. There are source encoder, quantizer and entropy encoder. Compression is achieved by assigning a linear transform to de-correlate the image data, quantizing the result transform coefficients and entropy coding of the quantized values [3]. & S. Sudhakar Ilango sudhakar.ilango@gmail.com; sudhakarilangos@gmail.com V. Seenivasagam yespee1094@yahoo.com R. Madhumitha madhuperu@gmail.com 1 Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu 641 008, India 2 Department of IT, National Engineering College, Kovilpatti, Thoothukudi, Tamil Nadu 628 503, India 123 Cluster Computing https://doi.org/10.1007/s10586-018-1982-9