The current paper presents an effective deblocking algorithm for block-based coded images using singularity detection in a wavelet transform. Blocking artifacts appear periodically at block boundaries in block-based coded images. The local maxima of a wavelet transform modulus detect all singularities, including blocking artifacts, from multiscale edges. Accordingly, the current study discriminates between a blocking artifact and an edge by estimating the Lipschitz regularity of the local maxima and removing the wavelet transform modulus of a blocking artifact that has a negative Lipschitz regularity exponent. Experimental results showed that the performance of the proposed algorithm was objectively and subjectively superior.

We proposed 3D-mesh watermarking using CEGI distribution that has robustness against mesh simplification, cropping, vertex randomization, and rotation and which does not need the original model for detection the watermark. The proposed algorithm embeds the watermark bits into the normal vector direction of meshes that are mapped into the cells that have the large magnitude of complex weight in patch CEGIs. The watermark can be detected based on the known center point of each patch and the rank table of the cell in each patch instead of original model. Experimental results verified that the proposed algorithm has robustness against the attacks.

This paper proposes a new blocking artifact reduction algorithm using an adaptive filter based on classifying the block boundary area. Generally, block-based coding, such as JPEG and MPEG, introduces blocking and ringing artifacts to an image, where the blocking artifact consists of grid noise, staircase noise, and corner outliers. In the proposed method, staircase noise and corner outliers are reduced by a 1D low-pass filter. Next, the block boundaries are divided into two classes based on the gradient of the pixel intensity in the boundary region. For each class, an adaptive filter is applied so that the grid noise is reduced in the block boundary regions. Thereafter, for those blocks with an edge component, the ringing artifact is removed by applying an adaptive filter around the edge. Finally, high frequency components are added to those block boundaries where the natural characteristics have been lost due to the adaptive filter. The computer simulation results confirmed a better performance by the proposed method in both the subjective and objective image qualities.