Yunhui Shi

Beijing University of Technology, Peping, Beijing, China

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Publications (41)5.2 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Screen contents with complex structure contain random combination of texts, graphics and camera-captured images, which makes them difficult to be compressed efficiently by traditional video codecs. In this paper, we propose a 2-D dictionary based scheme to exploit the repeated patterns on screen content. In the proposed scheme, the current block is predicted from the reconstructed region using a hash-based block searching scheme. A hierarchical two-level hash based searching scheme is designed to find the best matching block for each block. The first-level hash function is used to search the blocks similar to the current block in the constructed 2-D dictionary. The second-level hash function is used to update the 2-D dictionary, which filters out the identical blocks from the blocks found using the first-level hash function. The proposed scheme is incorporated into HEVC framework as an additional mode. Experimental results show that the proposed scheme achieves significantly coding performance improvements on screen contents compared with HEVC.
    2014 Data Compression Conference (DCC); 03/2014
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    ABSTRACT: Compressive sensing (CS) theory, which has been widely used in magnetic resonance (MR) image processing, indicates that a sparse signal can be reconstructed by the optimization programming process from non-adaptive linear projections. Since MR Images commonly possess a blocky structure and have sparse representations under certain wavelet bases, total variation (TV) and wavelet domain ℓ1 norm regularization are enforced together (TV-wavelet L1 method) to improve the recovery accuracy. However, the components of wavelet coefficients are different: low-frequency components of an image, that carry the main energy of the MR image, perform a decisive impact for reconstruction quality. In this paper, we propose a TV and wavelet L2–L1 model (TVWL2–L1) to measure the low frequency wavelet coefficients with ℓ2 norm and high frequency wavelet coefficients with ℓ1 norm. We present two methods to approach this problem by operator splitting algorithm and proximal gradient algorithm. Experimental results demonstrate that our method can obviously improve the quality of MR image recovery comparing with the original TV-wavelet method.
    Journal of Visual Communication and Image Representation 02/2013; 24(2):187–195. · 1.20 Impact Factor
  • Zhen Zhang, Yunhui Shi, Baocai Yin
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    ABSTRACT: Since Magnetic resonance(MR) Images commonly possess a blocky structure and have sparse representations under certain wavelet bases, total variation (TV) and wavelet domain ℓ1 norm regularization are often enforced together (TV-wavelet method) to improve the recovery accuracy. However, this model ignores that a family of wavelet coefficients has a natural grouping of its components. In this paper, we propose a new TV-Group sparse model which combines TV and wavelet domain group sparse penalty. The corresponding algorithm based on composite splitting method is employed to approach this TV-Group sparse model. Experimental results show that our model can obviously improve both objective and subjective qualities of MR image recovery comparing with the TV-wavelet model.
    Multimedia and Expo (ICME), 2013 IEEE International Conference on; 01/2013
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    ABSTRACT: The aim of this paper is to obtain the sparse property of signals better, a multi-directional adaptive sparse model and a recovery algorithm for it in compressive sensing are proposed. The multi-directional autoregressive model could use the local statistical correlation and texture directions of image to represent signal sparsely. In a transform based codec framework, the transform matrix is regarded as a measurement matrix. The traditional inverse transform in a decoder is replaced by the multidirectional adaptive sparse model. Simulation results over a wide range of images show that the proposed technique can improve the reconstruction quality of JPEG.
    Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology 01/2013; 39(3).
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    ABSTRACT: Sparse representation has been proved to be very efficient in machine learning and image processing. Traditional image sparse representation formulates an image into a one dimensional (1D) vector which is then represented by a sparse linear combination of the basis atoms from a dictionary. This 1D representation ignores the local spatial correlation inside one image. In this paper, we propose a two dimensional (2D) sparse model to much efficiently exploit the horizontal and vertical features which are represented by two dictionaries simultaneously. The corresponding sparse coding and dictionary learning algorithm are also presented in this paper. The 2D synthesis model is further evaluated in image denoising. Experimental results demonstrate our 2D synthesis sparse model outperforms the state-of-the-art 1D model in terms of both objective and subjective qualities.
    Multimedia and Expo (ICME), 2013 IEEE International Conference on; 01/2013
  • Yunhui Shi, Na Qi, Baocai Yin, Wenpeng Ding
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    ABSTRACT: Analysis sparse model has been successfully used for a variety of tasks such as image denoising, deblurring, and most recently compressed sensing, so it arouses much attention. K-SVD is a mature dictionary learning approach for the analysis sparse model. However, it represents images as one dimension signals, which results in mistakes of spatial correlations. In this paper, we propose a novel analysis sparse model, where analysis dictionary derived from two analysis operators which act on an image, leading to a sparse outcome. And a two dimensional K-SVD (2D-KSVD) is proposed to train the analysis sparse dictionaries. Experiments on image denoising validate that the proposed analysis dictionary can express more image spatial and frequency characteristics and by using the dictionary, the two dimension analysis sparse model outperforms the traditional analysis model in terms of PSNR.
    Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing; 12/2012
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    ABSTRACT: In order to show the realistic 3D mesh in geometry image-based 3D mesh compression, in addition to coding geometry image, normal-map image is usually required to code. But normal-map image are difficult to compress because it captures more details of the original mesh, and it has less spatial correlation between pixels than geometry image. This paper proposes a novel coding framework to solve this problem, we effectively predict the normal-map image based on the correlation between geometry image and normal-map image, and we also utilize the strong correlation among three components of normal-map image to improve the predicting accuracy. In this framework we only need to code geometry image and residual image which generated from normal-map image and its prediction. Experimental results show that comparing with the method which coding geometry image and normal-map image using JPEG2000 directly, our coding framework not only improves the coding efficiency of geometry images and normal-map images, but also enhances the realistic effect of 3D mesh significantly.
    01/2012;
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    ABSTRACT: This paper proposes a new method of inter prediction based on low-rank matrix completion. By collection and rearrangement, image regions with high correlations can be used to generate a low-rank or approximately low-rank matrix. We view prediction values as the missing part in an incomplete low-rank matrix, and obtain the prediction by recovering the generated low-rank matrix. Taking advantage of exact recovery of incomplete matrix, the low-rank based prediction can exploit temporal correlation better. Our proposed prediction has the advantage of higher accuracy and less extra information, as the motion vector doesn't need to be encoded. Simulation results show that the bit-rate saving of the proposed scheme can reach up to 9.91% compared with H.264/AVC. Our scheme also outperforms the counterpart of the Template Matching Averaging (TMA) prediction by 8.06% at most.
    Multimedia and Expo Workshops (ICMEW), 2012 IEEE International Conference on; 01/2012
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    ABSTRACT: The latest video coding standard H.264/AVC outperforms previous standards in terms of coding efficiency at cost of higher runtime complexity. When RDO is used, the most time-consuming process in a H.264/AVC encoder is mode decision, where all the intra/inter modes are tested to find the optimal coding mode. In this paper, we present a fast intra mode decision scheme, which first detects the texture direction and only tests a subset of intra modes consistent with detected direction. Experimental results demonstrate that the proposed scheme significantly reduces the overall encoding time with negligible coding performance loss.
    Proceedings of the 2011 Third International Workshop on Education Technology and Computer Science - Volume 01; 03/2011
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    ABSTRACT: In this paper, we give an analytical model of the compression error of down-sampled compression based on wavelet transform, which explains why down-sampling before compression can improve coding performance. And we approximate the missing details due to down-sampling and compression by using the linear combination of a set of basis vectors with L1 norm. Then we propose a down-sampled and high frequency information approximated coding scheme and apply it to natural images, and achieve gains of both subjective quality and objective quality compared with JPEG2000.
    Journal of Computational and Applied Mathematics 01/2011; 236:675-683. · 0.99 Impact Factor
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    ABSTRACT: Mode dependent directional transform (MDDT) can improve the coding efficiency of H.264/AVC but it also brings high computation complexity. In this paper we present a new design for implementing fast MDDT transform through integer lifting steps. We first approximate the optimal MDDT by a proper transform matrix that can be implemented with butterfly-style operation. We further factorize the butterfly-style transform into a series of integer lifting steps to eliminate the need of multiplications. Experimental results show that the proposed fast MDDT can significantly reduce the computation complexity while introducing negligible loss in the coding efficiency. Due to the merit of integer lifting steps, the proposed fast MDDT is reversible and can be implemented on hardware very easily.
    J. Visual Communication and Image Representation. 01/2011; 22:721-726.
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    ABSTRACT: H.264/AVC adopts intra coding technique to reduce spatial redundancies. In order to achieve better coding performance, there are nine candidate modes for 4×4 block unit to be selected by Rate-Distortion Optimization(RDO) process in the H.264/AVC encoder-side. However, too many intra candidate modes undoubtedly increase the transmitted bits that represent them. To solve this problem, a novel method for reducing the number of intra modes is proposed in this paper. For each 4 × 4 block, our algorithm can cluster the nine candidate modes into several groups and selects one mode from each group as a representative of all the modes in this group, so the cost of transmission can be reduced significantly because of the reduction of number of candidate modes. Experimental results show that the proposed method achieves 3.2% bit-rate reduction on average for various video sequences. The improvement of coding performance can be up to 7% bit-rate reduction at low bit rate, averagely.
    18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011; 01/2011
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    ABSTRACT: Transform-based image codec follows the basic principle: the reconstructed quality is decided by the quantization level. Compressive sensing (CS) breaks the limit and states that sparse signals can be perfectly recovered from incomplete or even corrupted information by solving convex optimization. Under the same acquisition of images, if images are represented sparsely enough, they can be reconstructed more accurately by CS recovery than inverse transform. So, in this paper, we utilize a modified TV operator to enhance image sparse representation and reconstruction accuracy, and we acquire image information from transform coefficients corrupted by quantization noise. We can reconstruct the images by CS recovery instead of inverse transform. A CS-based JPEG decoding scheme is obtained and experimental results demonstrate that the proposed methods significantly improve the PSNR and visual quality of reconstructed images compared with original JPEG decoder.
    Journal of Computational and Applied Mathematics 01/2011; 236:812-818. · 0.99 Impact Factor
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    ABSTRACT: Intra prediction plays an important role in reducing the spatial redundancy for intra frame encoding in H.264/AVC. In this paper, we propose a low-rank matrix completion based intra prediction to improve the prediction efficiency. According to the low-rank matrix completion theory, a low-rank matrix can be exactly recovered from quite limited samples with high probability under mild conditions. After moderate rearrangement and organization, image blocks can be represented as low-rank or approximately low-rank matrix. The intra prediction can then be formulated as a matrix completion problem, thus the unknown pixels can be inferred from limited samples with very high accuracy. Specifically, we novelly rearrange the encoded blocks similar to the current block to generate an observation matrix, from which the prediction can be obtained by solving a low-rank minimization problem. Experimental results demonstrate that the proposed scheme can achieve averagely 5.39% bit-rate saving for CIF sequences and 4.21% for QCIF sequences compared with standard H.264/AVC.
    IEEE 13th International Workshop on Multimedia Signal Processing (MMSP 2011), Hangzhou, China, October 17-19, 2011; 01/2011
  • Zhen Zhang, Yunhui Shi, Baocai Yin
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    ABSTRACT: Existing image codec technologies are based on transform which make image signal can be compressed, while quantization has been used to control bit rates. Compressive sensing (CS), which is a novel signal processing and recovery method, can be applied to image decoding to replace inverse transform reconstruction. This paper proposes an error estimate method based on equalization quantization noise model for image codec. Due to the robust character of CS, it can upgrade the quality of reconstruction when error has been estimated accurately. With designed equalization matrix, a new norm constraint which can enhance the quality of CS recovery significantly has been shown. A CS-based JPEG decoding scheme based on quantization error estimate is also presented, and experimental evidence exhibits more gains over CS reconstruction without error estimation and original JPEG decoder.
    01/2011;
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    ABSTRACT: Textures in many images or video scenes are difficult to code because of the large amount of visible detail. This paper proposes an image coding approach to solve this problem, in which we incorporate image decomposition and texture synthesis technology into the image coding framework. The key idea of our approach is to first decompose the original image into cartoon component u and texture component v with different basic characteristics, and then to synthesize the selected texture regions in texture component v. The cartoon component u and the non-synthetic regions in texture component v are compressed by JPEG. Experimental results show bit-rate savings of over 30% compared with JPEG at similar visual quality levels.
    Proceedings fo the Picture Coding Symposium, PCS 2010, Nagoya, Japan, 8-10 December, 2010; 01/2010
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    ABSTRACT: Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. We make efforts in sparse representation and its implementation to solve the problem. In the representation, image patches are decomposed into two structure and texture components represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Finally a super-resolution construction based on multi-space sparse representation is proposed. Experiment results demonstrate that the proposed method significantly improve the PSNR and visual quality of reconstructed high-resolution image.
    Multimedia Tools and Applications 01/2010; · 1.01 Impact Factor
  • Guodong Jing, Yunhui Shi, Bing Lu
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    ABSTRACT: In this paper, we propose a novel method for solving single-image super-resolution problems. Firstly, using the human visual perception and image gradient features, the image total variation is decomposed into structural components and texture components. Based on the theory about sparse signal representation, we used K-SVD method to generate ultra-complete dictionary and to achieve the reconstruction of the texture component. Then the super-resolution reconstruction of the whole original low resolution image is realized by fused them with the bi-cubic interpolated image reconstruction of the structural components. The proposed method, without external image database support, brings in the whole image information while depends on the fixed -K neighborhood. It can upgrade the fitting performance of the existing methods, and enhance mole detail image information, also improve the reconstructed image quality.
    01/2010;
  • Xiaowei Sun, Baocai Yin, Yunhui Shi
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    ABSTRACT: Texture-synthesis based video coding is a newly emerged video coding method which is employed to reduce the bit-rate of texture region. With the increasing amount of different terminals currently available, scalability is also an important concern of the state-of-the-art video coding standards. Due to its intensive calculation instinct, in texture-synthesis based video coding scheme, more emphases should be put on its ability of bit-rate saving, and the reduction of computational complexity. In this paper, we propose a scalable strategy for texture-synthesis based video coding methodology, which mainly focus on bit-rate and computation scalability. Two parameters are used to control bit-rate and computational complexity of the decoder. Experimental results show that the proposed strategy can provide our texture-synthesis based codec with significant scalability of bit-rate and computational complexity.
    11/2009;
  • Xiaowei Sun, Baocai Yin, Yunhui Shi
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    ABSTRACT: In this paper, a new low cost texture-synthesis based video coding scheme is presented for H.264/AVC, which is well-suited for a specific class of stochastic texture like water, and cloud, etc. We give a solution to two key problems that bother the application of texture synthesis in video coding for a long time. One is the synthesis cost of the decoder, and the other is the visible artifact of the output synthetic texture. In our proposal, to preserve the texture's high-order information and against the global motion, sequential texture region is copied to the synthesis region in a considerable scale in contrast with the existing synthesis method used in video coding. In this way, the tremendous computational cost is transferred to the encoder, which can greatly simplify the decoder design. This scheme has been integrated into H.264/AVC, and the real-time synthesis can be achieved at similar visual quality levels compared with H.264/AVC, with large amount of bit-rate saved.
    Image and Signal Processing, 2009. CISP '09. 2nd International Congress on; 11/2009