Chwen-Jye Sze

Cocktail Watermarking on Images

A novel image protection scheme named “cocktail watermarking” improves over current spread-spectrum watermarking approaches. Two watermarks, which play complementary roles, are simultaneously embedded into an original image. The new watermarking scheme has the characteristic that, no matter what an attack is, at least one watermark typically survives well and can be detected. Results of extensive experiments indicate that our cocktail watermarking scheme is effective in resisting various attacks.

Combined watermarking for image authentication and protection

A novel combined watermarking scheme for image authentication and protection is proposed in this paper. By utilizing the publicly available wavelet-based just noticeable distortion (JND) values, the hidden watermark is designed to carry the host images information such that blind watermark detection becomes possible. The watermarks are embedded using the previously proposed cocktail watermarking technique and are extracted by a quantization process. According to the polarities and the differences of the hidden and the extracted watermarks, the fragility and robustness of a watermark can be measured, respectively, such that both content authentication and copyright protection are achieved simultaneously.

Multiscale edge detection on range images via normal changes

A new edge detection technique based on detection of normal changes is proposed. Most of the existing range image-based edge detection algorithms base their detection, criterion on depth or curvature changes. However, the depth change-based approach does not have keen sensitivity in detecting roof (or crease) edges, and the curvature change-based approach suffers from a complicated and tedious principal curvature derivation process. Using normal changes as a detecting criterion, on the other hand, the existence of an edge can be easily detected, even when the change across a boundary is slight. Experimental results using both synthetic and real images demonstrate that the proposed method can efficiently detect both step and roof edges.

Fractal image coding system based on an adaptive side-coupling quadtree structure

A new fractal-based image compression system, based on a so-called Adaptive Side-Coupling Quadtree (ASCQ) structure, is proposed. The proposed system consists of three processes: a preprocessing, a compression and a decompression process. In the compression process, the original image is represented by an ASCQ structure. The set of Iterated Function System (IFS) codes, which is usually derived in the encoding process, can be calculated directly from this tree structure. Using these IFS codes, an image which is similar to the original one can be reconstructed. Unlike traditional methods, which have separate domain and range pools, the proposed ASCQ structure simultaneously contains the domain pool and range pool. Since the proposed ASCQ is an adaptive structure, the number of IFS codes will be variant depending on their corresponding original images. Experimental results show that the ASCQ structure is indeed an efficient structure for the fractal-based image compression system.

A new image flux conduction model and its application to selective image smoothing

A discrete image flux conduction equation which is completely new in this field is proposed. The new approach starts with formulating a discrete image flux conduction equation based on the concept of heat conduction theory. Based on this discrete equation, the status change at a time point can be directly computed from its spatial neighborhood. To more accurately estimate an image flux, we have used an orthogonal wavelet basis to approximate the gradient of the intensity at each point. Since the proposed approach is discrete by nature, it is not necessary to formulate a continuous PDE to fit the discrete image data set. Furthermore, introduction of different numerical methods to solve the PDE can also be avoided. Since the proposed approach does not require that a PDE be solved, it is therefore more efficient and accurate than the conventional methods. Experimental results obtained using both synthetic signals and real images have demonstrated that the proposed model could effectively handle the selective image smoothing problem.

Selective image smoothing via dyadic wavelet-based conduction equation

We propose a new dyadic wavelet-based conduction approach for selective image smoothing. In our approach, a nonlinear conductivity function is considered in the wavelet-based function decomposition and reconstruction process. Since the proposed approach does not require one to solve a PDE, it is therefore more efficient and accurate than the conventional nonlinear diffusion/conduction-based methods. Experimental results using both 1-D synthetic data and a real image demonstrated that the proposed method can efficiently remove noises and preserve real data.

Multiscale Edge Detection via Normal Changes

A new edge detection technique based on detection of normal changes is proposed. Most of the existing range image-based edge detection algorithms base their detection criterion on depth or curvature changes. However, the depth change-based approach does not have keen sensitivity in detecting roof ( or crease ) edges, and the curvature change-based approach suffers from a complicated and tedious principal curvature derivation process. Using normal changes as a detecting criterion, on the other hand, the existence of an edge can be easily detected, even when the change across a boundary is slight. Experimental results using both synthetic and real images demonstrate that the proposed method can efficiently detect both step and roof edges.