The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
Exploiting the JPEG Compression Scheme for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Communications of the ACM
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Compressed-domain techniques for image/video indexing and manipulation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Spatial Statistics for Content Based Image Retrieval
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Subband-Based, Memory-Efficient JPEG2000 Images Indexing in Compressed-Domain
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Multimedia Information Retrieval and Management: Technological Fundamentals and Applications
Multimedia Information Retrieval and Management: Technological Fundamentals and Applications
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
PicToSeek: combining color and shape invariant features for image retrieval
IEEE Transactions on Image Processing
Color object indexing and retrieval in digital libraries
IEEE Transactions on Image Processing
Texture-based medical image retrieval in compressed domain using compressive sensing
International Journal of Bioinformatics Research and Applications
Hi-index | 0.01 |
Retrieving images compressed by different algorithms typically involves a pre-processing operation to decompress them onto the spatial domain from which features are extracted for further analysis. Our objective is to investigate common features that can be found in JPEG-compressed and JPEG 2000-compressed images so that image indexing can be done directly in their respective compressed domains. A fundamental difference between JPEG and JPEG 2000 is their transforms; the former uses a block-based discrete cosine transform (BDCT) while the latter uses a wavelet transform (WT). Direct comparison on BDCT blocks and WT subbands cannot reveal their relationship. By employing our proposed subband-filtering model, the BDCT coefficients can be concatenated to form structures similar to WT subbands. Our theoretical studies show that the concatenated BDCT and WT filters share common characteristics in terms of passband regions, magnitude and energy spectra. In particular, their low-pass filters are identical for Haar wavelets and highly similar for other wavelet kernels. Despite the fact that compression can affect features that can be extracted, our experimental results confirm that common features can always be extracted from JPEG- and JPEG 2000-compressed domains irrespective of the values of the compression ratio and the types of WT kernels used. As a result, similar JPEG-compressed and JPEG 2000-compressed images can be retrieved from one another without requiring a full decompression.