Bit-depth expansion using Minimum Risk Based Classification

Abstract

Bit-depth expansion is an art of converting low bit-depth image into high bit-depth image. Bit-depth of an image represents the number of bits required to represent an intensity value of the image. Bit-depth expansion is an important field since it directly affects the display quality. In this paper, we propose a novel method for bit-depth expansion which uses Minimum Risk Based Classification to create high bit-depth image. Blurring and other annoying artifacts are lowered in this method. Our method gives better objective (PSNR) and superior visual quality as compared to recently developed bit-depth expansion algorithms.