Title :
Kernel matching pursuits prioritization of wavelet coefficients for SPIHT image coding
Author :
Chang, Shaorong ; Carin, Lawrence
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
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.
Keywords :
data compression; hidden Markov models; image classification; image coding; image matching; image segmentation; image texture; least mean squares methods; trees (mathematics); wavelet transforms; HMT mixture model; KMP method; MSE; SPIHT; Set Partitioning In Hierarchical Trees; hidden Markov trees; image classification; image coding; kernel matching pursuits; minimum mean-squared error; progressive image compression; texture segmentation; wavelet coefficient prioritization; wavelet sub-band; Bit rate; Decoding; Hidden Markov models; Image coding; Image segmentation; Kernel; Matching pursuit algorithms; Parameter estimation; Signal processing algorithms; Wavelet coefficients;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
Print_ISBN :
0-7803-8484-9
DOI :
10.1109/ICASSP.2004.1326628
