In this paper, we propose a new watermarking scheme which combines both the digital holography and the digital watermarking techniques. The watermark is transformed into a digital phase-shifting hologram based on the Mach-Zehnder interferometer architecture and then is embedded into the specific subbands in the wavelet transform of the host image. By inversely transforming the wavelet coefficients in the subbands, the watermark can be successfully extracted with acceptable degradation. Simulations are performed to evaluate the performance of this new scheme.

Current trademark classification procedure adopts the Vienna approach developed by World Intellectual Property Organization (WIPO). It has the following drawbacks. 1) Tremendous amount of human efforts are required to classify and annotate each trademark submitted for registration; the time required to complete the registration would increasing as the number of registered trademarks increases. 2) Same trademark may be classified into different classes by two human classifiers. 3) It is difficult to classify trademarks with abstractive contents by human classifier. For the above reasons, it is important to improve the existing trademark classification system by replacing human classifier with effective and precise computer algorithms. As we review previous researches on trademark indexing and retrieval, we find that shape information together with profile and contents of trademark plays an important role in determining the trademark similarity. In this paper, we develop an effective feature for trademark image using block feature index (BFI), the concept is borrowed from vector quantization. For performance evaluation purpose, 3000 trademark images in MEPG-7 were classified into 10 categories for experiments. We compare the recall rate and precision for the proposed BFIC algorithm and two well-known methods: Hu's 7 moments (H7M) and Zernike moments (ZKM), the proposed BFIC method outperforms the other two methods. The performance can be even better by cascading the proposed BFIC and ZKM.

Conventional fractal coding techniques explore only the self-similarity in an image (i.e., the intra-image similarity) to achieve compression. The paper proposes a fractal mating coding (FMC) method that also utilizes the similarity between two or more images (i.e., the inter-image similarity). In the proposed FMC method, two or more images are mutually encoded and decoded. This provides the security/protection property that only mutually encoded images can be correctly decoded. Computer experiments for the proposed FMC method are provided and the simulation result shows that the peak signal-to-noise ratio can be improved, on average, by more than 1 dB under the same bit rate for the test images

Arbitrarily shaped coding is an important tool to achieve MPEG-4, which is a object-based coding standard. We proposed an efficient shaped coding method which enhances the coding efficiency of conventional padding techniques in the object-based video coding where the shape information is provided. The boundary block-searching algorithm (BBS) is applied to the boundary blocks of a VOP (video object plane) which consists of both background and object pixels. Actually, boundary blocks have strong correlation even though they are located apart. For an input boundary block, we search the closest block (only object pixels) from the previously encoded data using the BBS algorithm. After searching, the boundary block is represented as a position vector. This process reduces the number of boundary blocks to be DCT and makes the bit rate small. Experimentation has been done on various video sequences under different test conditions and verifies significant coding efficiency improvement.

In this paper, we propose gradient match fractal vector quantizers (GMFVQs) and side match fractal vector quantizers (SMFVQs), which are two classes of finite state fractal vector quantizers (FSFVQs), for the image coding framework. In our previous work, we proposed the noniterative fractal block coding (FBC) technique to improve the decoding speed and the coding performance for conventional FBC techniques. To reduce the number of bits for denoting the fractal code of the range block, the concepts of the gradient match vector quantizers (GMVQs) and the side match vector quantizers (SMVQs) are employed to the noniterative FBC technique. Unlike ordinary vector quantizers, the super codebooks in the proposed GMFVQs and SMFVQs are generated from the affine-transformed domain blocks in the noniterative FBC technique. The codewords in the state codebook are dynamically extracted from the super codebook with the side-match and gradient-match criteria. The redundancy in the affine-transformed domain blocks is greatly reduced and the compression ratio can be significantly increased. Our simulation results show that 15%-20% of the bit rates in the noniterative FBC technique are saved by using the proposed GMFVQs.

The domain pool design is one of the dominant issues which affect the coding performance of fractal image compression. In this paper, we employ the LBG algorithm and propose a block averaging method to design the efficient domain pools based on a proposed iteration-free fractal image codec. The redundancies between the generated domain blocks are reduced by the proposed methods. Therefore, we can obtain the domain pools that are more efficient than those in the conventional fractal coding schemes and thus the coding performance is improved. On the other hand, the iteration process in the conventional fractal coding scheme not only requires a large size of memory and a high computation complexity but also prolongs the decoding process. The proposed iteration-free fractal codec can overcome the problems above. In computer simulation, both the LBG-based and block-averaging methods for the domain pool design in the proposed iteration free scheme achieve excellent performances. For example, based on the proposed block-averaging method, the decoded Lena image has at least a 0.5 dB higher PSNR (under the same bit rate) and an eight-time faster decoding speed than the conventional fractal coding schemes that require iterations.

An image compression scheme based on the finite-state fractal block coding (FS-FBC) is proposed. Since the FS algorithm has been successfully employed by the vector quantization (VQ) techniques and there are many similarities between VQ and FBC scheme, we employ the FS algorithm on FBC technique to determine the fractal codes. The next-state functions for the domain pool size, contrast scaling factor, isometry, and mean are designed. In computer simulation, we first independently investigate the FS effect of each parameter and then combine all methods to test the overall effect. We obtain a reconstructed Lena image with 33.9 dB PSNR at 0.42 bit/pixel, while the result of original FBC scheme is 31.4 dB at 0.6 bit/pixel. The simulation results show that our FS methods can be successfully employed on the FBC technique to reduce the bit rate and the image quality is still preserved.

Fractal image coding is an effective method to eliminate the image redundancy through piecewise self transformability called fractal codes on a block-wise sequence. The fractal code is a set of contractive affine transforms defined as a sequence of parent and child block transformations. To improve the performance when a parent range block experiences large error, we usually partition the parent range block into four children (smaller) square blocks. In this paper, a shape-adaptive fractal coding (SAFC) based on the edge information is proposed. In SAFC, each parent range block is first partitioned into nonsquare (rectangle or triangle) children blocks which are to be fractal encoded. If the coded image quality does not improve, then the traditional square partition is executed. In SAFC, no additional computations are required to obtain the edge orientation of a range block. Instead, the edge orientation (of a range block) is obtained during the isometries calculation between a range block and all its possible domain blocks. According to our simulation, SAFC achieve 33.9 dB PSNR at 0.49 bit/pixel (bpp), while the conventional fractal coding achieves 34.0 dB at 0.55 bpp. In addition, we mathematically show that the proposed detector does find the correct edge orientation of blocks under certain conditions.