=Paper=
{{Paper
|id=None
|storemode=property
|title=Content-based Retrieval of Compressed Images
|pdfUrl=https://ceur-ws.org/Vol-706/invited1.pdf
|volume=Vol-706
|dblpUrl=https://dblp.org/rec/conf/dateso/Schaefer11
}}
==Content-based Retrieval of Compressed Images==
<pdf width="1500px">https://ceur-ws.org/Vol-706/invited1.pdf</pdf>
<pre>
     Content-based Retrieval of Compressed Images

                                      Gerald Schaefer

                              Department of Computer Science
                                 Loughborough University
                                   Loughborough, U.K.
                                gerald.schaefer@ieee.org



           Abstract. Content-based image retrieval allows search for pictures
           in large image databases without keyword or text annotations. Much
           progress has been made in deriving useful image features with most of
           these features being extracted from (uncompressed) pixel data. How-
           ever, the vast majority of images today are stored in compressed form
           due to limitations in terms of storage and bandwidth resources. In this
           paper, we therefore investigate a different approach, namely that of com-
           presseddomain image retrieval, and present some compressed-domain
           image retrieval techniques that we have developed over the past years.
           In particular, a method for retrieving images compressed by vector
           quantisation, that uses codebook information as image features, is pre-
           sented. Retrieval of losslessly compressed images obtained using lossless
           JPEG, can be retrieved using information derived from the Huffman
           coding tables of the compressed files. Finally, CVPIC, a 4-th criterion
           image compression technique is introduced and it is demonstrated that
           compressed-domain image retrieval based on CVPIC is not only able to
           match the performance of common retrieval techniques on uncompressed
           images, but even clearly outperforms these.


    Keywords: content-based image retrieval (CBIR), image compression, com-
    presseddomain image retrieval, vector quantisation, lossless JPEG, CVPIC




V. Snášel, J. Pokorný, K. Richta (Eds.): Dateso 2011, pp. 226–228, ISBN 978-80-248-2391-1.

</pre>