1 / 11

Lossless Recovery of a VQ Index Table with Embedded Secret Data

Lossless Recovery of a VQ Index Table with Embedded Secret Data. Chin-Chen Chang, Wen-Chuan Wu b, and Yu-Chen Hu Journal of Vision Communication and Image Representation, Vol. 18, 2007, pp. 207 – 216 Reporter: Jen-Bang Feng. VQ Compression. VQ compressed. Original image. Block size 2*2.

montana-irwin
Download Presentation

Lossless Recovery of a VQ Index Table with Embedded Secret Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lossless Recovery of a VQ Index Table with Embedded Secret Data Chin-Chen Chang, Wen-Chuan Wu b, and Yu-Chen Hu Journal of Vision Communication and Image Representation, Vol. 18, 2007, pp. 207–216 Reporter: Jen-Bang Feng

  2. VQ Compression VQ compressed Original image Block size 2*2 Size 512*512 24-bits per pixel  768 KBytes 256*256 indices 8-bits per index  64 KBytes  Codebook size 256 = 28 256*4*24-bit = 3 KBytes

  3. Embedding Secrets Original indices 0, 5, 4, 8, 20 • Extended Research: • Group clustering • Dynamic groups • CodeBook training Secret bits 1 0 1 0 0 Group 2 Group 1 Stego indices 1, 4, 5, 8, 20 CodeBook M. Jo, K.D. Kim, A digital image watermarking scheme based on vector quantization, IEICE Transactions on Information and Systems E85-D (6) (2002) 1054–1056.

  4. 1. The Pre-Processing Procedure • To reorganize the original VQ codebook of N codewords to facilitate the future embedding capacity

  5. 2. Clustering The most often used indices Cr, Cr+m, Cr+2m are closed to each other Only Cluster 1 is embedded with secrets Index number 0 is preserved for identity

  6. 3. Embedding Secrets Need only Floor( log2index/3) -bits

  7. Another Example Index number 0 and 31 are preserved for identity

  8. Number of Clustered Blocks • Only blocks are clustered. • B*3 blocks are one set

  9. Ex. Results Not reversible Binary images

  10. Ex. Results

  11. Conclusions • Reversible data hiding with VQ • Can not normally revealed until recover

More Related