Block-Classification-Based AMBTC with Neural Networks for Image Compression

Abstract

The world has recently witnessed a rapid revolution in multimedia signal processing. Images are one of the most widely used media that needs a large amount of data to be represented. Because of the restrictions of limited bandwidth and storage capacity, image compression is a necessity. AMBTC is a straightforward lossy image compression scheme, and studies are still being conducted to improve its performance. This paper incorporated AMBTC with block classification and artificial neural networks to lower the bitrate and preserve the image quality. The proposed scheme was benchmarked with the recent AMBTC techniques for greyscale images. According to the results, the proposed method significantly improved over conventional AMBTC by achieving 16% bitrate reduction while preserving 99.19% of AMBTC’s Peak Signal to Noise Ratio (PSNR).