In this paper, a very efficient image denoising scheme, which is called nonlocal means based on bidirectional principal component analysis, is proposed. Unlike conventional principal component analysis (PCA) based methods, which stretch a 2D matrix into a 1D vector and ignores the relations between different rows or columns, we adopt the technique of bidirectional PCA (BDPCA), which preserves the spatial structure and extract features by reducing the dimensionality in both column and row directions. Moreover, we also adopt the coarse-to-fine procedure without performing nonlocal means iteratively. Simulations demonstrated that, with the proposed scheme, the denoised image can well preserve the edges and texture of the original image and the peak signal-to-noise-ratio is higher than that of other methods in almost all the cases.

In this paper, a new adaptive scanning scheme, which is called local prediction based adaptive scanning (LPBAS), is proposed for discrete cosine transform (DCT) based image compression techniques including JPEG and H.264/AVC intra coding. The conventional zigzag scan order is widely used in image and video coding standards, but it ignores the statistical properties of DCT blocks and has limited performance. In this paper, the LPBAS scheme is proposed to achieve the entropy coding gain, where the scan order patterns are adaptively generated and updated based on the statistics of local neighboring DCT blocks. The proposed scheme improves the efficiency of the two image coding systems, JPEG and the H.264/AVC intra coding system. Simulation results showed that the proposed scheme indeed outperforms the zigzag scanning method and other existing adaptive scanning methods.

In this paper, a new algorithm which adopts the techniques of double prediction and the Pareto probability model was applied to encode the DC term in the JPEG compression process. Conventionally, the DC term was encoded by differential coding, i.e., the difference of the DC values between the current block and the previous block. In this paper, we first use the DC terms of four adjacent blocks to predict the current DC value. We then further use the prediction error of the four adjacent blocks to estimate the variance of the prediction error of the current block. We call it the double prediction algorithm. Next, the Pareto distribution is applied to model the probability distribution of the prediction error. Simulation results show that, with the proposed algorithms, the data size required for DC terms is significantly reduced by 25% ~ 60% and a much higher compression rate can be achieved.

Properly designed context models can increase the compression gain. In this paper, we propose a new lossless image coding scheme with two proposed algorithms: nonlocal context modeling and adaptive prediction (NCMAP). Since structural self-similarity often exists in natural images, we use the probability to measure the similarity between the powers of prediction errors for the pixels to be coded. Furthermore, the spatial distance and the intensity range are also considered for context generation. Moreover, a prediction scheme that adaptively combines the weighted edge-directed prediction (WEDP) and the nonlocal predictor (NLP) is also proposed. With the proposed context generating and prediction strategies, better compression performances can be achieved. Simulations show that the proposed scheme outperforms existing methods for lossless image compression.

Image interpolation is important for computer vision. Most of the existing image interpolation methods are based on the optimization in the mean square error (MSE) sense. In this paper, we incorporate the structural similarity (SSIM) based metric into the framework of the nonlocal edge-directed image interpolation (NLEDI) method. In the proposed algorithm, a missing pixel is interpolated using the weighted average of neighboring patches where the weights are determined by the SSIM-based metric instead of the MSE measurement. Simulations show that our proposed structural similarity-based NLEDI (SSNLEDI) scheme outperforms existing image interpolation methods and has higher PSNR values and better visual qualities.

This paper proposes joint-probability-based adaptive Golomb coding (JPBAGC) to improve the performances of the Golomb family of codes, including Golomb coding (GC), Golomb-Rice coding (GRC), exp-Golomb coding (EGC), and hybrid Golomb coding (HGC), for image compression. The Golomb family of codes is ideally suited to the processing of data with geometric distribution. Since it does not require a coding table, it has higher coding efficiency than Huffman coding. In this paper, we find that there are many situations in which the probability distribution of data is not only geometric, but also depends on the probability distribution of the other data. Accordingly, we used the joint probability of generalizing the Golomb family of codes and exploiting the dependence between neighboring image data. The proposed JPBAGC improves the efficiency of many image and video compression standards, such as the joint photographic experts group (JPEG) compression scheme and the H.264-intra JPEG-based image coding system. Simulation results demonstrate the superior coding efficiency of the proposed scheme over those of Huffman coding, GC, GRC, EGC, and HGC.

In the conventional JPEG algorithm, an image is divided into eight by eight blocks and then the 2-D DCT is applied to encode each block. In this paper, we find that, in addition to rectangular blocks, the 2-D DCT is also orthogonal in the trapezoid and triangular blocks. Therefore, instead of eight by eight blocks, we can generalize the JPEG algorithm and divide an image into trapezoid and triangular blocks according to the shapes of objects and achieve higher compression ratio. Compared with the existing shape adaptive compression algorithms, as we do not try to match the shape of each object exactly, the number of bytes used for encoding the edges can be less and the error caused from the high frequency component at the boundary can be avoided. The simulations show that, when the bit rate is fixed, our proposed algorithm can achieve higher PSNR than the JPEG algorithm and other shape adaptive algorithms. Furthermore, in addition to the 2-D DCT, we can also use our proposed method to generate the 2-D complete and orthogonal sine basis, Hartley basis, Walsh basis, and discrete polynomial basis in a trapezoid or a triangular block.

A method of natural image classification by an effective color quadtree segmentation together with a more effective codebook with the color local thresholding classifier for content-based image retrieval (CBIR) is proposed. The vector quantization (VQ) based image retrieval schemes have good performance, but the importance of color edge intensive blocks is neglected. Our proposed method has two main improvements. First, quadtree segmentation based on both hue and gray-level information is applied to classify the blocks into the homogeneous and high-detail ones. Second, a color local thresholding classifier is proposed to further classify the high-detail blocks based on edge information. Simulation results show that our proposed scheme outperforms the existing methods, including the Quadtree CVQ-based scheme, the VQ-based scheme, and other methods.

With the fast development of multimedia, it is crucial to find the way to search image database effectively. The vector quantization (VQ) based image retrieval method is popular in recent years. In this paper, we propose the quadtree classified vector quantization (QCVQ) scheme to improve the VQ method by exploiting the visual importance of image blocks and using the edge information to describe the content of each block efficiently. Moreover, we also apply the adaptive block size. The simulation results show that, compared with the previous image retrieval algorithms using VQ and chromaticity moments (CM), our proposed scheme has obviously better average retrieval rate and higher average precision.

Coefficient scanning plays an important role in block-based image and video coding standards, such as JPEG, MPEG, and the latest H.264/AVC. In these coding standards, the zigzag scanning method is used for image / frame coding and the field scanning method is used for field coding. Although these scanning methods can achieve acceptable coding efficiency, they do not take the statistical properties of the quantized coefficients of each block into consideration. Therefore, coding redundancy still exists. In this paper, we propose a novel adaptive zigzag scanning scheme, which is called the context-based adaptive coefficient scanning method, and apply it in the lossy JPEG baseline algorithm. Simulation results show that our proposed method achieves better coding efficiency than both the conventional zigzag scanning method and the two-stage zigzag scanning (TSZS) method adopted in JPEG.