Projection-based Context Modeling for Reversible Integer Wavelet Transforms
نویسندگان
چکیده
Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard 1]. In this paper, a projection technique is described that exploits non-orthogonality among transform basis vectors to derive nal lifting steps for wavelet transforms. Additionally , projection-based predictions of detail coeecients are used in an adaptive lifting scheme which varies the-nal prediction step of the lifting-based transform based on a modeling context. The adaptive projection-based transform yields lower rst-order entropy of transform coeecients and better compression performance than current lifting-based transforms.
منابع مشابه
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...
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