Direction-adaptive context modeling for sign coding in embedded wavelet image coder

The conventional embedded wavelet image coder exploiting the adjacent neighbors is limited to efficiently compress the sign coefficients, since the wavelet coefficients are highly correlated along the dominant image features, such as edges or contours. To solve the problem, in this paper, we propose the direction-adaptive sign context modeling by adaptively exploiting the neighbors suitable to the dominant image features. Experimental results show that the sign coding based on the proposed context modeling reduces the sign bits up to 5.5% compared to the conventional sign coding method.

1. INTRODUCTION

The wavelet transform has become the most powerful technique for image compression during last decades and been adopted by JPEG2000 standard due to its high coding performance, scalability and anti-blocking effects [1]. Conventionally, wavelet transform for 2-D image is performed by cascading two separable 1-D transforms horizontally and vertically. Due to its limited representation for edges and textures, recently, adaptive directional wavelet transforms providing enough vanishing moments along dominant image features have been developed [2]–[5].