A Robust Identification Scheme for JPEG XR Images with Various Compression Ratios

A robust scheme for identifying JPEG XR coded images is proposed in this paper. The aim is to identify the images that are generated from the same original image under various compression ratios. The proposed scheme is robust against a difference in compression ratios, and does not produce false negative matches in any compression ratio. A new property of the positive and negative signs of lapped biorthogonal transform coefficients is considered to robustly identify the images. The experimental results show the proposed scheme is effective for not only still images, but also video sequences in terms of the querying such as false positive, false negative and true positive matches.

[1]  Mrinal K. Mandal,et al.  Efficient image indexing techniques in the JPEG2000 domain , 2004, J. Electronic Imaging.

[2]  F. Dufaux,et al.  The JPEG XR image coding standard [Standards in a Nutshell] , 2009, IEEE Signal Processing Magazine.

[3]  Hitoshi Kiya,et al.  Identification of JPEG 2000 images in encrypted domain for digital cinema , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[4]  Hitoshi Kiya,et al.  Codestream-Based Identification of JPEG 2000 Images with Different Coding Parameters , 2012, IEICE Trans. Inf. Syst..

[5]  Andrew R. McIntyre,et al.  Exploring content-based image indexing techniques in the compressed domain , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).

[6]  Masaaki Fujiyoshi,et al.  Fast Method for Joint Retrieval and Identification of JPEG Coded Images Based on DCT Sign , 2007, 2007 IEEE International Conference on Image Processing.

[7]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting , 2010 .

[8]  Hitoshi Kiya,et al.  Two layer lossless coding of HDR images , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Hitoshi Kiya,et al.  A fixed-point implementation of tone mapping operation for HDR images expressed in floating-point format , 2014 .

[10]  Wan-Chi Siu,et al.  A fast approach for identifying similar features in retrieval of JPEG and JPEG2000 images , 2009 .

[11]  David S. Taubman,et al.  High performance scalable image compression with EBCOT. , 2000, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[12]  Hitoshi Kiya,et al.  Fast identification of JPEG 2000 images for digital cinema profiles , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Hitoshi Kiya,et al.  Hash-based identification of JPEG 2000 images in encrypted domain , 2012, 2012 International Symposium on Intelligent Signal Processing and Communications Systems.

[14]  Jianmin Jiang,et al.  Web-based image indexing and retrieval in JPEG compressed domain , 2004, Multimedia Systems.

[15]  Michael Shneier,et al.  Exploiting the JPEG Compression Scheme for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[17]  Masaaki Fujiyoshi,et al.  Fast and Robust Identification Methods for JPEG Images with Various Compression Ratios , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.