Progressive image data compression with adaptive scale-space quantization

Artur Przelaskowski

doi:10.1117/12.373450

20 December 1999 Progressive image data compression with adaptive scale-space quantization

Artur Przelaskowski

Proceedings Volume 3964, Internet Imaging; (1999) https://doi.org/10.1117/12.373450
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

Some improvements of embedded zerotree wavelet algorithm are considere. Compression methods tested here are based on dyadic wavelet image decomposition, scalar quantization and coding in progressive fashion. Profitable coders with embedded form of code and rate fixing abilities like Shapiro EZW and Said nad Pearlman SPIHT are modified to improve compression efficiency. We explore the modifications of the initial threshold value, reconstruction levels and quantization scheme in SPIHT algorithm. Additionally, we present the result of the best filter bank selection. The most efficient biorthogonal filter banks are tested. Significant efficiency improvement of SPIHT coder was finally noticed even up to 0.9dB of PSNR in some cases. Because of the problems with optimization of quantization scheme in embedded coder we propose another solution: adaptive threshold selection of wavelet coefficients in progressive coding scheme. Two versions of this coder are tested: progressive in quality and resolution. As a result, improved compression effectiveness is achieved - close to 1.3 dB in comparison to SPIHT for image Barbara. All proposed algorithms are optimized automatically and are not time-consuming. But sometimes the most efficient solution must be found in iterative way. Final results are competitive across the most efficient wavelet coders.

Citation Download Citation

Artur Przelaskowski "Progressive image data compression with adaptive scale-space quantization", Proc. SPIE 3964, Internet Imaging, (20 December 1999); https://doi.org/10.1117/12.373450

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