My Paper Presentation

Color QIM Watermarking In The JPEG-LS Coding Pipeline
Ms. R. Jayapriya, Ms. P.L. Mahalakshmi
Post Graduate Students, Cauvery College for Women, Tiruchirappalli – 620 018.
Email: jayapriya_rajendran@yahoo.co.in, pl.mahalakshmi@gmail.com

ABSTRACT
In this paper, we propose a watermarking method integrated in the JPEG-LS coding pipeline. It is based on Quantization Index Modulation on Digital Watermarking for color images using JPEG-LS “near lossless” compression standard. The watermark is capable of carrying such information as authentication or authorisation codes, or a legend essential for image interpretation. This capability is envisaged to find application in image tagging, copyright enforcement, counterfeit protection, and controlled access.

1. PREAMBLE
What is Digital Watermarking?
A digital watermark is a set of data embedded in a digital image, which may be used for a number of different purposes including copyright protection. A digital watermark can be detected only by appropriate software. Instead of “ensuring the authenticity or integrity of documents, as a digital signature or a digital seal does, a digital watermark aims to identify the origin, author, owner, usage rights, distributor, or authorized user of an image, video clip, or audio clip, even if the image or clip has been processed and distorted.”

Detection and Use of Digital Watermarking
“Digital watermarks are effective only when imperceptible; [they] cannot be visible when viewing a digitized photograph or audible when listening to a digitized sound recording. Otherwise, watermarks would obstruct the quality of the image or the music and, furthermore, facilitate removal by copyright infringers.” Furthermore, in order to be effective, watermarks must remain recognizable even if the document has undergone several conversions including copying, editing, scanning, and rescanning.
Although digital watermarks are unlikely to provide protection against theft, they effectively deter copyright piracy of still images, video and audio files transmitted over the Internet, intranets, digital satellite and digital cable. For example, an intentional attempt to delete watermarks will result in a serious degradation in the quality of the work, thus, giving viewers or listeners less incentive to pirate the inferior copy.
Second, “a copyright owner may be able to trace the source of an unauthorized copy posted on the Internet, thereby exposing that user inducing the abuse to liability.” Finally, because an imitated work would be easily identifiable from the original, copyright owner may have more confidence to distribute his own work on the internet.”

2. THE PROBLEM
Quantization Index Modulation
We wish to embed a message m 0 {1,2,……,2N Rm }, sometimes called a digital watermark, in some host signal vector x 0 UN, where Rm is the embedding rate in bits per host signal sample.

Figure 1: Quantization Index Modulation

Quantization Index Modulation (QIM) embedding functions arise by defining an ensemble of quantizers q(-;m), one quantizer in the ensemble for each possible value of m. Then, s(x, m) = q(x; m). An example is shown in Figure 1 for the case where N =1, Rm = 1, and the quantizers are uniform, scalar quantizers. One can decode, for example, by determining whether y is closer to a o point ( = 1) or to a x point ( = 2). Thus, the x and o points represent both source codewords for representing x and channel codewords for communicating m.


Color Quantization
Color quantization or color image quantization is a process that reduces the number of distinct colors used in an image, usually with the intention that the new image should be as visually similar as possible to the original image. Computer algorithms to perform color quantization on bitmaps have been studied since the 1970s.
The name "color quantization" is primarily used in computer graphics research literature; in applications, terms such as optimized palette generation, optimal palette generation, or decreasing color depth are used.
Color quantization is critical for displaying images with many colors on devices that can only display a limited number of colors, usually due to memory limitations, and enables efficient compression of certain types of images.

Color is a visual perception of the light in the visible region of the electromagnetic wave spectrum incident on the retina.
Since the retina has three types of photoreceptors that respond to different parts of the visible spectrum, three components are necessary and sufficient to specify a color. It has long been found that mean square error is a very poor measure of color difference in many tristimulus color spaces, such as RGB, CIEXYZ, YUV, and so forth. Color distributions in these tristimulus spaces are nonuniform in that the Euclidean distance between any two colors is usually not closely correlated with the associated perceptual difference. In case a nonuniform color space is uniformly quantized, the fixed color distance between any two colors of the quantized color space will result in large variation in perceptual difference, and perceptible distortion if the quantizer stepsize is large. In the proposed watermarking scheme, embedding and extraction of color watermarks are accomplished by color quantization.
The image with the watermark embedded is actually the dequantization of a quantized image, of which quantization indices are disturbed by watermark information. To guarantee the transparency of the embedded watermark, the color difference between a pixel and its watermarked counterpart should be uniform and must not be perceptible throughout the whole image. To attain this goal, uniform quantization must be carried out in a uniform color space with the quantizer stepsize tuned to result in imperceptible color difference between any two adjacent colors in the quantized color space.

3. ANALYSIS
Watermark Embedding
The process of embedding color watermarks in color images is described by the functional block diagram shown in Figure 2. The host image is first transformed into the CIELab color space where the color gamut of the host image is analyzed. Before watermark embedding, one or more sets of color tables are adopted as a part of the private key to render the watermark to multiple presentations, and the watermark is repeated to forma watermark image having the same dimension as the host image.



Figure 2: Watermark embedding process.




Watermark Extraction
The process of watermark extraction is described by the functional block diagram shown in Figure 3, where the target image is first transformed to the CIE-Lab color space. The uniform quantizer used for watermark embedding is rebuilt by the private key information. As shown in Figure 4, the information organized in the private key contains the color gamut of the host image, quantizer stepsize, permutation key, and tables of colors for multiple representations. The number of bits required for representing these watermarking parameters can be as minimal as 189 bits.
Figure 3: Watermark Extraction Process

Figure 4: Information contained in the private key.

Lossless Color Quantization
In this paper, color quantization is performed in the CIE-Lab color space where the color difference is more closely correlated with the perceptual difference. Any two colors with the same Euclidean distance in this space have approximately the same perceptual difference. A useful rule of thumb in this color space is that any two colors can be distinguished in a sense if their color distance is greater than 3, the so-called just noticeable color difference (JNCD). The stepsize of the proposed uniform quantizer is thus determined in a way that the color difference between any two neighboring colors centroids should not be perceivable, or not to exceed the JNCD. By considering the masking effect mainly due to local variations in luminance magnitude, the quantizer stepsize can be set to be larger than JNCD.
Colors that are perceptually indistinguishable from a particular color in the uniform color space form a sphere with a radius equal to the JNCD (Figure 5a). As shown in Figure 5b, the spherical space can be approximated by a cubic space which can be further partitioned into 3×3×3 uniformcubic subspaces.
Figure 5: (a) The spherical subspace defined by color C and the JNCD in the CIE-Lab space, within which all colors are perceptually indistinguishable from the color C; (b) the 27 uniform cubic subspaces that accommodate the spherical subspace.

Figure 6: (a) The watermark represented by 8 different colors; (b) the watermark represented by two colors where the code (000) is assigned to represent color white and the rest of the codes color black.
JPEG-LS
JPEG, Joint Photographic Experts Group, in addition to their well-known lossy image compression techniques, JPEG and JPEG 2000, also have three standards for lossless compression (of which JPEG-LS has a lossy mode):
Lossless JPEG was developed as a late addition to JPEG in 1993, using a completely different technique from the lossy JPEG standard. It uses a predictive scheme based on the three nearest (causal) neighbors (upper, left, and upper-left), and entropy coding is used on the prediction error. It is not supported by the standard Independent JPEG Group libraries, and was never widely adopted.
JPEG-LS was developed with the aim of providing a low complexity "near lossless" image compression standard that could be able to offer better compression efficiency than lossless JPEG. Part 1 of this standard was finalized in 1999; and when released, Part 2 of this standard will introduce extensions such as arithmetic coding.
JPEG-LS also provides a lossy mode where the maximum absolute error can be controlled by the encoder. Compression for JPEG-LS is generally much faster than JPEG 2000 and much better than the original lossless JPEG standard.
JPEG 2000 includes a lossless mode based on a special integer wavelet filter. JPEG 2000's lossless mode runs more slowly and has often worse compression ratios than JPEG-LS on artificial and compound images. JPEG 2000 fares better than the UBC implementation of JPEG-LS on digital camera pictures. JPEG 2000 is also scalable, progressive, and more widely supported.

4. ADVANTAGES
ü Imperceptibility: The embedded watermarks are imperceptible both perceptually as well as statistically and do not alter the aesthetics of the multimedia content that is watermarked. The watermarks do not create visible artifacts in still images, alter the bit rate of video or introduce audible frequencies in audio signals.
ü Robustness: Depending on the application, the digital watermarking technique can support different levels of robustness against changes made to the watermarked content. If digital watermarking is used for ownership identification, then the watermark has to be robust against any modifications. The watermarks should not get degraded or destroyed as a result of unintentional or malicious signal and geometric distortions like analog-to-digital conversion, digital-to-analog conversion, cropping, resampling, rotation, dithering, quantization, scaling and compression of the content. On the other hand, if digital watermarking is used for content authentication, the watermarks should be fragile, i.e., the watermarks should get destroyed whenever the content is modified so that any modification to content can be detected.
ü Inseparability: After the digital content is embedded with watermark, separating the content from the watermark to retrieve the original content is not possible.
ü Security: The digital watermarking techniques prevent unauthorized users from detecting and modifying the watermark embedded in the cover signal. Watermark keys ensure that only authorized users are able to detect/modify the watermark.

5. PROSPECTIVE WORK
Ø Other Image compression standards can be tried out
Ø Better embedding methods
Ø Concentration on media other than images
Ø Multi-Dimensional Digital Watermarking


6. CONCLUSION
Digital watermarking is an effective technique for embedding rights information in digital multimedia data. Digital watermarking is an emerging technology which is critical for IP rights management and it is expected to have huge commercial potential when it gets widely deployed in consumer electronic devices.

Digital watermarking technology and its applications have huge potential in consumer electronics industry. Digital watermark technology can be used in consumer electronic devices like digital still camera, digital video camera, set-top box (STB), DVD players, MP3 players, etc., for various applications like providing controlled access (pay-per-view broadcasts), preventing illegal replication and watermark embedding (embedding ownership information in images captured in digital still/video cameras).

Through color quantization, color watermarks are carried by the quantization indices of the host image in the uniform color space. Watermark transparency is achieved by perceptually lossless color quantization and modification in quantization indices.

To watermark color images, the image need to be compressed and in this paper we have tried it in a lossless manner. Therefore, the JPEG-LS standard can be used.


7. REFERENCES
[1] D. De Schrijver, R. De Sutter, P. Lambert, and R. Van de Walle “Lossless Image Coding based on Fractals” from Proceeding (479) Signal and Image Processing, (Belgium) - 2005
[2] Peter Meerwald “International Federation for Information Processing, Conference on Communications and Multimedia Security Darmstadt”, Germany, May 21 - 22, 2001.
[3] Lidia Pedraza, Comment, “Entertainment Law Initiative 2000 Legal Writing Contest”.
[4] Jian Zhao, Look, It's Not There: Digital Watermarking is the Best Way to Protect Intellectual Property from Illicit Copying, BYTE.COM, Jan. 1997 http://www.byte.com/art/9701/sec18/art1.htm
[5] Brian Chen and Gregory W. Wornell, “Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding”, Sorrento, Italy, June 25-30, 2000
[6] Wikipedia, The FREE Encyclopedia, “Color Quantization”, http://en.wikipedia.org/wiki/Color_quantization, 21st June 2007 (last modified)
[7] Chun-Hsien Chou, Tung-Lin Wu, “Embedding Color Watermarks in Color Images”, EURASIP Journal on Applied Signal Processing 2003:1, 32-40
[8] Wikipedia, The FREE Encyclopedia, “Lossless JPEG”, http://en.wikipedia.org/wiki/Lossless_JPEG, 16th August 2007 (last modified)
[9] Wipro Technologies, “Digital Watermarking: A Technology Overview”, http://www.wipro.com/dsp, 2001