Learning based approach for image compression

Abstract

Data compression is a process of storing same information with less data or space in computer memory. There are many image compression techniques that are available for storing images with less storage space. Minimizing storage space minimizes the bandwidth required for transmission. In the proposed algorithm first level Discrete Wavelet Transformation (DWT, with Daubechies wavelets db4 as a mother wavelet) is applied on original image after which only low resolution coefficients are retained. Further Embedded Zero Tree Wavelet based algorithm (EZW) [10] is applied for best image quality at the given bit rate. We are using a set of images as database. For every input image Content Based Image Retrieval (CBIR) [7] technique is applied on database which results in some images, having similar content. At the receiver a learning based approach is used to decompress from resulted database images. Structure Similarity Index Measurement (SSIM) [15] an image quality assessment is used for similarity check. Inverse DWT is applied to get the estimate of the original. This is a lossy compression and results are compared with JPEG [13] and JPEG2000 [8] compression.