Hsin-Hui Chen

Adaptive Golomb Code for Joint Geometrically Distributed Data and Its Application in Image Coding

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.

Real time accelerometer-based gait recognition using adaptive windowed wavelet transforms

This paper presents a real time gait recognition system using the wavelet transform. The activity signal is acquired from three-axis accelerometers on mobile phones. It is first decomposed into wavelet coefficients with eight levels. Several statistical measures, such as power, mean, variance, energy, and the energy of neighbor difference, are calculated from these coefficients. Furthermore, the adaptive window size is adopted to well fit the footstep of each person. The selected features are also adjusted adaptively to improve the accuracy. The simulation results show that the proposed method has reliable recognition accuracy both in the real-time and the long-term cases.

Two-Dimensional Orthogonal DCT Expansion in Trapezoid and Triangular Blocks and Modified JPEG Image Compression

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.

Image retrieval based on quadtree classified vector quantization

In this paper, a color image retrieval scheme based on quadtree classified vector quantization (QCVQ) is proposed. This scheme not only captures intra-block correlation but also exploits the visual importance of image blocks to efficiently describe the content of images in a compressed domain. In the proposed algorithm, a query image is first divided by quadtree segmentation and then classified into smooth and high-detail blocks. For high-detail blocks, the local thresholding classifier with 28 edge binary templates is employed to extract a variety of visually important regions which are edge intensive. After all of the blocks in the image are encoded by the pre-trained QCVQ codebook, the indices in the compressed domain are obtained. Finally, the frequencies of indices are counted to build the index histogram as a feature of the query image. Simulation results demonstrate that our proposed scheme yields the better retrieval performance compared to the well-known vector quantization (VQ)-based image retrieval method and three other techniques. These results show that quadtree segmentation and edge style classification are indeed helpful for improving the performance of content-based image retrieval.

Structural similarity-based nonlocal edge-directed image interpolation

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.

Context-based adaptive zigzag scanning for image coding

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.

Quadtree classified vector quantization based image retrieval scheme

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.

Nonlocal means image denoising based on bidirectional principal component analysis

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.

Efficient DC term encoding scheme based on double prediction algorithms and Pareto probability models

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.

Local prediction based adaptive scanning for JPEG and H.264/AVC intra coding

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.