JPEG2000 compressed domain image retrieval using context labels of significance coding and wavelet autocorrelogram

Navin Angkura; Supavadee Aramvith; Supakorn Siddhichai

doi:10.1117/12.740123

Translator Disclaimer

10 September 2007 JPEG2000 compressed domain image retrieval using context labels of significance coding and wavelet autocorrelogram

Navin Angkura, Supavadee Aramvith, Supakorn Siddhichai

Author Affiliations +

Proceedings Volume 6777, Multimedia Systems and Applications X; 67770O (2007) https://doi.org/10.1117/12.740123
Event: Optics East, 2007, Boston, MA, United States

Abstract

JPEG has been a widely recognized image compression standard for many years. Nevertheless, it faces its own limitations as compressed image quality degrades significantly at lower bit rates. This limitation has been addressed in JPEG2000 which also has a tendency to replace JPEG, especially in the storage and retrieval applications. To efficiently and practically index and retrieve compressed-domain images from a database, several image features could be extracted directly in compressed domain without having to fully decompress the JPEG2000 images. JPEG2000 utilizes wavelet transform. Wavelet transforms is one of widely-used to analyze and describe texture patterns of image. Another advantage of wavelet transform is that one can analyze textures with multiresolution and can classify directional texture pattern information into each directional subband. Where as, HL subband implies horizontal frequency information, LH subband implies vertical frequency information and HH subband implies diagonal frequency. Nevertheless, many wavelet-based image retrieval approaches are not good tool to use directional subband information, obtained by wavelet transforms, for efficient directional texture pattern classification of retrieved images. This paper proposes a novel image retrieval technique in JPEG2000 compressed domain using image significant map to compute an image context in order to construct image index. Experimental results indicate that the proposed method can effectively differentiate and categorize images with different texture directional information. In addition, an integration of the proposed features with wavelet autocorrelogram also showed improvement in retrieval performance using ANMRR (Average Normalized Modified Retrieval Rank) compared to other known methods.

Citation Download Citation

Navin Angkura, Supavadee Aramvith, and Supakorn Siddhichai "JPEG2000 compressed domain image retrieval using context labels of significance coding and wavelet autocorrelogram", Proc. SPIE 6777, Multimedia Systems and Applications X, 67770O (10 September 2007); https://doi.org/10.1117/12.740123

ACCESS THE FULL ARTICLE