On context-based entropy coding of block transform coefficients

It has been well established that state-of-the-art wavelet image coders outperform block transform image coders in the rate-distortion (R-D) sense by a wide margin. An often asked question is: how much of the coding improvement is due to the transform and how much is due to the encoding strategy? A notable observation is that each block transform coefficient is highly correlated with its neighbors within the same block as well as its neighbors within the same subband. Current block transform coders suffer from poor context modeling and fail to take full advantage of intra- and inter-block correlation in both space and frequency sense. This paper presents a simple, fast and efficient adaptive block transform image coding algorithm based on high-order space-frequency, context modeling. Despite the simplicity constraints, coding results show that the proposed codec achieves competitive R-D performances comparing to the best wavelet codecs in the current literature.