The gray-code kernels (GCK) family, which has Walsh Hadamard transform on sliding windows as a member, is a family of kernels that can perform image analysis efficiently using a fast algorithm, such as the GCK algorithm. The GCK has been successfully used for pattern matching. In this paper, we propose that the G4-GCK algorithm is more efficient than the previous algorithm in computing GCK. The G4-GCK algorithm requires four additions per pixel for three basis vectors independent of transform size and dimension. Based on the G4-GCK algorithm, we then propose the segmented GCK. By segmenting input data into L(s) parts, the SegGCK requires only four additions per pixel for 3L(s) basis vectors. Experimental results show that the proposed algorithm can significantly accelerate the full-search equivalent pattern matching process and outperforms state-of-the-art methods.

Artifacts commonly exist in the coded image and video. Non-local Kuan's (NLK) filter has achieved the state-of-the-art performance on deartifacting the coded image, but there is no literature incorporating it into video scenarios. In the paper, we propose two loop filters, quadtree-based NLK (QNLK) filter and quadtree-based overcomplete NLK (QOCNLK) filter, for video coding. In the proposed techniques, NLK and overcomplete NLK filters are used to restore quantized residual transform coefficients, and quadtree-based signaling strategy is used for adaptive filtering control. Experimental results show that the proposed techniques achieve significant bitrate saving and visual quality improvement, compared with H.264/AVC High Profile.

Pattern matching is a widely used procedure in signal processing, computer vision, image and video processing. Recently, methods using Walsh Hadamard Transform (WHT) and Gray-Code kernels (GCK) are successfully applied for fast transform domain pattern matching. This paper introduces strip sum on the image. The sum of pixels in a rectangle can be computed by one addition using the strip sum. Then we propose to use the orthogonal Haar transform (OHT) for pattern matching. Applied for pattern matching, the algorithm using strip sum requires O(log u) additions per pixel to project input data of size N × N onto u 2-D OHT basis while existing fast algorithms require O(u) additions per pixel to project the same data onto u 2-D WHT or GCK basis. Experimental results show the efficiency of pattern matching using OHT.

Blocking artifacts exist in images and video sequences compressed to low bit rates using block discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, a novel DCT domain technique is presented in this paper. Firstly, a new FIR Wiener filter which exploits the dependence of neighboring DCT coefficients based on the linear minimum mean-square-error (LMMSE) criterion is proposed. Then we apply the new FIR Wiener filter twice in a dual adaptive filtering structure to restore each quantized DCT coefficient. In addition, an efficient parameter estimation method is proposed for the designed filter. Experimental results show that the performance of the proposed method is comparable to the state-of-the-art methods but has low computational complexity.

Edge detection plays an important role in digital image processing. Based on the non-decimated wavelet which is shift-invariant, in this paper, we develop a new edge detecting technique using 3-D Hidden Markov Model. Our proposed model can not only capture the relationship of the wavelet coefficients inter-scale, but also consider the intra-scale dependence. A computationally efficient maximum likelihood (ML) estimation algorithm is employed to compute parameters and the hidden state of each coefficient is revealed by maximum a posteriori (MAP) estimation. Experimental results of natural images are provided to evaluate the algorithm. In addition, the proposed model has the potential to be an efficient multi-scale statistical modeling tool for other image or video processing tasks.