Abstract:
The Set Partitioning In Hierarchical Trees (SPIHT), an efficient wavelet-based progressive image-compression scheme, is oriented to minimize the mean-squared error (MSE) ...Show MoreMetadata
Abstract:
The Set Partitioning In Hierarchical Trees (SPIHT), an efficient wavelet-based progressive image-compression scheme, is oriented to minimize the mean-squared error (MSE) between the original and decoded imagery. In this paper, we use the kernel matching pursuits (KMP) method to estimate the importance of each wavelet sub-band for distinguishing between different textures segmented by an HMT mixture model. Before the SPIHT coding, we weight the wavelet coefficients, with the goal of achieving improved image-classification results at low bit rates. A modified SPIHT algorithm is proposed to improve the coding efficiency. The performance of the original SPIHT and the modified SPIHT algorithms is compared.
Date of Conference: 17-21 May 2004
Date Added to IEEE Xplore: 30 August 2004
Print ISBN:0-7803-8484-9
Print ISSN: 1520-6149