Scalable Image Coding with Projection-Based Context Modeling

Many state-of-the-art wavelet image coders use nonorthog-onal transforms for both lossy and lossless wavelet image coding 1, 2, 3, 4]. In this paper, a projection prediction is described that capitalizes on the non-orthogonality of wavelet transform basis vectors to improve the prediction of high-frequency coeecients. For lossy wavelet coders, the prediction yields improved context modeling and up to .2 dB coding improvement. For lossless integer wavelet coders, the prediction can be used as an extra lifting step during the transform, and results in a lower rst-order entropy of wavelet coeecients and lower subsequent coding rate than current integer wavelet transforms.