KEYWORDS: Digital watermarking, Image processing, Visual process modeling, Image quality, Resistance, Human vision and color perception, Sensors, Image restoration, Feature extraction, Wavelets
This paper proposes a novel perceptual watermarking scheme operating in a Hermite transform domain. To achieve an
acceptable level of watermark invisibility, masking properties of the Human Vision system (HVS) are exploited in the
extraction of relevant local image features (texture, smooth regions, edges) for watermark embedding purpose. Many
other works suggest the use of wavelets or contourlets. In our case, image features are extracted efficiently from the
Hermite transform image representation which agrees with the Gaussian derivative model of the human visual
perception. The resulting weighing mask is used to adapt the watermark strength to image regions during the embedding
process.
In order to ensure watermark resistance to global affine geometric attacks (rotation, scaling, translation and shearing) the
design of the watermarking scheme is modified, mainly, by incorporating a normalization procedure. Image
normalization, a means to achieve invariance to geometric transformations, is well known in computer vision and pattern
recognition areas. In this new design, both watermark embedding and detection are carried out in the Hermite transform
domain of moment-based normalized images.
A sequence of tests is conducted on various images. Many removal attacks (JPEG compression, additive noise and
filtering) as well as geometric attacks are applied from the Checkmark benchmark. Experimental results show the
effectiveness of the whole scheme in achieving its goals in terms of watermark invisibility and robustness.
The ability to flexibly access coded video data at different resolutions or bit rates is referred to as scalability. We are concerned here with the class of methods referred to as pyramidal embedded coding in which specific subsets of the binary data can be used to decode lower- resolution versions of the video sequence. Two key techniques in such a pyramidal coder are the scan-conversion operations of down-conversion and up-conversion. Down-conversion is required to produce the smaller, lower-resolution versions of the image sequence. Up- conversion is used to perform conditional coding, whereby the coded lower-resolution image is interpolated to the same resolution as the next higher image and used to assist in the encoding of that level. The coding efficiency depends on the accuracy of this up-conversion process. In this paper techniques for down-conversion and up-conversion are addressed in the context of a two-level pyramidal representation. We first present the pyramidal technique for spatial scalability and review the methods used in MPEG-2. We then discuss some enhanced methods for down- and up-conversion, and evaluate their performance in the context of the two-level scalable system.
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