An efficient framework for increasing image quality using DRN Bi-layer enfolded compressor

Abstract

Lossy /lossless image compression technique results will either improve the quality of image with reduced compression ratio or degrades the image with better compression ratio. But on dealing with the combined lossy/lossless techniques, they provide the output with better image quality and high compression rate, however, the techniques failed to obtain better efficiency due to less predictive nature. Hence, to acquire an image having better compression rate with improved predictive nature, a novel framework is introduced in our proposed work. It uses a Deep Residual Network Bi-Layer Enfolded Compressor (DRN-BLEC) to enhance the image quality. In order to obtain better compression rate, the Mexican Meyer Hat Wavelet Transform (MMHWT) is used. Similarly, the well knowledge quantization is achieved by utilizing DRN for learning; it is then followed by the Lloyds quantization technique that groups the feature values by quantizing it, which in turn boost-up the resolution of the image. Consequently with DRN, the process of encoding and decoding doesn’t requires to be done separately since two residual layer of the DRN are trained to encode and decode the images, which thereby reduces the time required for compression process. Thus a compressed image with high resolution is obtained without redundancy, moreover with high compression ratio. The architecture of the DRN-BLEC is implemented in MATLAB and the respective result structure is validated.