Image Compression by Parameterized-Model Coding of Wavelet Packet Near-Best Bases

Top-down tree search algorithms with non-additive information cost comparisons as decision criteria have recently been proposed by Taswell for the selection of near-best bases in wavelet packet transforms. Advantages of top-down non-additive near-best bases include faster computation speed, smaller memory requirement, and extensibility to biorthogonal wavelets in addition to orthogonal wavelets. A new compression scheme called parameterized-model coding was also proposed and demonstrated for one-dimensional signals. These methods are extended here to two-dimensional signals and applied to the compression of images. Significant improvement in compression while maintaining comparable distortion is demonstrated for parameterized-model coding relative to quantized-scalar coding. In general, the lossy compression scheme is applicable for low bit rate coding of the M largest packets of wavelet packet decompositions with wavelet packet basis libraries and the M atoms of matching pursuit decompositions with time-frequency atom dictionaries.