An embedded hierarchical image coder using zerotrees of wavelet coefficients

This paper describes a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance. A fully embedded code represents a sequence of binary decisions that distinguish an image from the `null´ image. Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly. Also, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream. The algorithm consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images, but requires absolutely no training, no pre-stored tables or codebooks, and no prior knowledge of the image source. It is based on four key concepts: (1) wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression achieved via adaptive arithmetic coding