Showing papers by "Guangming Shi published in 2010"

Compress Compound Images in H.264/MPGE-4 AVC by Exploiting Spatial Correlation

Abstract: Compound images are a combination of text, graphics and natural image. They present strong anisotropic features, especially on the text and graphics parts. These anisotropic features often render conventional compression inefficient. Thus, this paper proposes a novel coding scheme from the H.264 intraframe coding. In the scheme, two new intramodes are developed to better exploit spatial correlation in compound images. The first is the residual scalar quantization (RSQ) mode, where intrapredicted residues are directly quantized and coded without transform. The second is the base colors and index map (BCIM) mode that can be viewed as an adaptive color quantization. In this mode, an image block is represented by several representative colors, referred to as base colors, and an index map to compress. Every block selects its coding mode from two new modes and the previous intramodes in H.264 by rate-distortion optimization (RDO). Experimental results show that the proposed scheme improves the coding efficiency even more than 10 dB at most bit rates for compound images and keeps a comparable efficient performance to H.264 for natural images.

Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

Abstract: As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact that natural images are intrinsically sparse in some domain. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a pre-collected dataset of example image patches, and then for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image non-local self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.

Super-resolution with nonlocal regularized sparse representation

Abstract: The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the l1-norm sparsity prior into the super-resolution reconstruction process. In this paper, we present a new SR based image super-resolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.

Intra frame coding with template matching prediction and adaptive transform

Abstract: For natural images, there are usually repeating similar contents but hard to be well predicted locally. Prediction using template matching is an effective technology to exploit such a non-local correlation. In this paper, we propose an alternative scheme to further exploit the non-local correlation. In the proposed scheme, template matching is also used to search for probable similar references to the current block to be coded. We then use these references to train an adaptive transform, which most likely reflects the statistical characteristic of the current block. The proposed scheme can further exploit correlation between the current block and more possible references. Compared to the scheme that only integrates prediction by template matching, the proposed scheme shows improvement about 0.45dB PSNR increase or 9.3% bit saving on average, which leads to 1dB's gain or 19.5% bit saving on average compared to the state-of-the-art scheme without using template matching.

Morphological dilation image coding with context weights prediction

Abstract: This paper proposes an adaptive morphological dilation image coding with context weights prediction. The new dilation method is not to use fixed models, but to decide whether a coefficient needs to be dilated or not according to the coefficient's predicted significance degree. It includes two key dilation technologies: 1) controlling dilation process with context weights to reduce the output of insignificant coefficients, and 2) using variable-length group test coding with context weights to adjust the coding order and cost as few bits as possible to present the events with large probability. Moreover, we also propose a novel context weight strategy to predict coefficient's significance degree more accurately, which serves for two dilation technologies. Experimental results show that our proposed method outperforms the state of the art image coding algorithms available today.

Spatial-Temporal Color Video Reconstruction From Noisy CFA Sequence

Abstract: Single-sensor digital video cameras use a color filter array (CFA) to capture video and a color demosaicking (CDM) procedure to reproduce the full color sequence. The reproduced video frames suffer from the inevitable sensor noise introduced in the video acquisition process. This paper presents a spatial-temporal denoising and demosaicking scheme that works without explicit motion estimation. We first perform patch based denoising on the mosaic CFA video. For each CFA patch to be denoised, similar patches are selected within a local spatial-temporal neighborhood. The principal component analysis is performed on the selected patches to remove noise. We then apply an initial single-frame CDM to the denoised CFA data, and subsequently post-process the demosaicked frames by exploiting the spatial-temporal redundancy to reduce the color artifacts. The experimental results on simulated and real noisy CFA sequences demonstrate that the proposed spatial-temporal CFA video denoising and demosaicking scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structures.

Morphological dilation image coding with context weights prediction

Abstract: This paper proposes an adaptive morphological dilation image coding with context weights prediction. The new dilation method is not to use fixed models, but to decide whether a coefficient needs to be dilated or not according to the coefficient's predicted significance degree. It includes two key dilation technologies: (1) controlling dilation process with context weights to reduce the output of insignificant coefficients and (2) using variable-length group test coding with context weights to adjust the coding order and cost as few bits as possible to present the events with large probability. Moreover, we also propose a novel context weight strategy to predict a coefficient's significance degree more accurately, which can be used for two dilation technologies. Experimental results show that our proposed method outperforms the state of the art image coding algorithms available today.

Color demosaicking with sparse representations

Abstract: Color demosaicking is an ill-posed inverse problem of image restoration. The performance of a color demosaicking algorithm depends on how thoroughly it can exploit domain knowledge to confine the solution space for the underlying true color image. We propose a sparsity-based l 1 minimization technique for color demosaicking that exploits both interband and intra-band sparse representations of natural images. In some of most challenging cases of color demosaicking, the proposed technique outperforms those published in the literature by a significant margin in both PSNR and visual quality.

Lossy-to-lossless image compression based on multiplier-less reversible integer time domain lapped transform

Abstract: In this paper, a reversible integer to integer time domain lapped transform (RTDLT) is introduced. TDLT can be taken as a combination of time domain pre- and post-filter modules with discrete cosine transform (DCT). Different from TDLT, the filters and DCT in our proposed RTDLT are realized from integer to integer by multi-lifting implementations after factorizing the filtering and transforming matrixes into triangular elementary reversible matrices (TERMs). Lifting implementations are realized by only shift and addition without any floating-point multiplier to reduce complexity. The proposed method can realize progressive lossy-to-lossless image compression with a single bit-stream. Simulation results show that RTDLT-based compression system obtains comparable or even higher compression-ratio in lossless compression than that of JPEG2000 and JPEG-LS, as well as gratifying rate distortion performance in lossy compression. Besides, RTDLT keeps low-complexity in hardware realization because it can be parallel implemented on the block level.

An improved model of pixel adaptive just-noticeable difference estimation

Abstract: A pixel-wise adaptive model for estimating the just-noticeable difference (JND) in spatial domain is proposed in this paper. As the human visual system (HVS) can be considered as a multichannel system, we assume that there exist two channels in the HVS, which deliver luminance adaption factor and texture masking factor, respectively. Both channels affect the JND threshold in a cooperative manner. The texture regions are with abundant redundancy and can tolerate much noise. The disorder degree and spatial masking of the texture are considered to estimate the texture masking effect, for deducing such JND threshold that coincides with the HVS. Finally, the luminance adaptation factor and texture masking factor are combined nonlinearly. Various experiments confirm the improved model has a better visual effect than models proposed before.

Design of Linear-Phase Nonuniform Filter Banks With Partial Cosine Modulation

Abstract: The majority of the existing work on designing nonuniform filter banks (NUFBs) cannot achieve linear-phase (LP) property, because of the high complexity associated with phase distribution. This correspondence proposes an idea of partial cosine modulation to obtain the LP property of NUFBs with rational sampling factors. It makes the efficient modulation technique possible to be used to design LP NUFBs. Except the separately designed lowpass and highpass analysis/synthesis filters, we obtain the bandpass by cosine modulation of several prototypes, worth of the name ?partial cosine modulation.? By analyzing the phase issue of significant aliasing terms, we derive the matching conditions to make the required LP NUFB achievable. With these criteria being satisfied, the design problem becomes that of several prototypes as well as the lowpass and highpass filters, leading to a less design effort. By using the proposed method, near-perfect-reconstruction LP NUFBs can be obtained in a simple and efficient way as demonstrated by examples.

Context-based adaptive image resolution upconversion

Abstract: We propose a practical context-based adaptive image resolution upconversion algorithm. The basic idea is to use a low- resolution (LR) image patch as a context in which the missing high- resolution (HR) pixels are estimated. The context is quantized into classes and for each class an adaptive linear filter is designed using a training set. The training set incorporates the prior knowledge of the point spread function, edges, textures, smooth shades, etc. into the upconversion filter design. For low complexity, two 1-D context- based adaptive interpolators are used to generate the estimates of the missing pixels in two perpendicular directions. The two direc- tional estimates are fused by linear minimum mean-squares weight- ing to obtain a more robust estimate. Upon the recovery of the miss- ing HR pixels, an efficient spatial deconvolution is proposed to deblur the observed LR image. Also, an iterative upconversion step is performed to further improve the upconverted image. Experimen- tal results show that the proposed context-based adaptive resolution upconverter performs better than the existing methods in both peak SNR and visual quality. © 2010 SPIE and IS&T.

Low code rate image compression method based on down sampling and interpolation

Abstract: The invention discloses a low code rate image compression method based on down sampling and interpolation, mainly overcoming the defects of low objective PSNR value and unclear texture edges in subjective image quality in the prior low code rate image compression. The low code rate image compression method comprises the following implementation steps: (1) carrying out Laplacian pyramid decomposition on an initial image and sampling under low-pass filtration to generate a low-frequency subband signal; (2) carrying out the adaptive direction coding of the low-frequency subband signal to generatethe compressed stream information of a low code rate compressed image; (3) carrying out the adaptive direction improving decoding of the compressed stream information of the low code rate compressedimage and generating a reconstructed low-frequency subband signal; and (4) carrying out directional wave interpolation recovery on the decoded low-frequency subband signal and generating a reconstructed image. The invention has the advantages of high objective PSNR value and more clear edge and texture details of low code rate compression and can be used for recovering real-time compressed image transmission with high quality.

Electrocardiogram signal lossless compression method based on PT conversion and linear prediction combination

Abstract: The invention discloses an electrocardiogram signal lossless compression method based on PT conversion and linear prediction combination, belonging to the technical field of data processing. The method comprises the following compression processes: carrying out PT conversion on an original electrocardiogram signal; subtracting a converted original signal value and an estimated value to obtain a residual error signal of the whole electrocardiogram signal; carrying out self-adaption variable grade RICE coding on the residual error signal to output a coding bit stream and finishing the lossless compression on the electrocardiogram signal; decoding the compressed bit stream; reconstructing the signal and finishing decompression. The lossless compression method ensures the information completion and accuracy of the compressed electrocardiogram signal and can be used for transmitting and storing the electrocardiogram signal.

Super-resolution imager via compressive sensing

Abstract: In this paper, we propose a novel imager that can acquire super-resolution (SR) images with significantly fewer sensors. The theoretical basis of this imager is compressive sensing (CS) theory, which calls for a measurement matrix with good properties for effective reconstruction, such as RIP [3]. Such a property indicates that entries of the received signal are effectively aliased. In our imager we use an optic effect called spherical aberration to achieve such aliased measurement of light intensity (the signal), thus realizing an ideal measurement matrix. The original image can then be efficiently reconstructed through the filternating Direction Method (ADM) [2]. The implementation of the proposed imager needs only replace an ordinary lens with a spherical lens of large curvature, with almost no additional cost, in contrast with existing complex systems, such as the single pixel camera using the micro-mirror device [12]. Simulation results show that despite its simplicity, the performance of the proposed imager is comparable with traditional CS models (most of which are difficult for physical implementation). Further, since the lens is a linear shift-invariant (LSI) system, FFT can be incorporated into the ADM algorithm to accelerate the reconstruction [8], adding to its advantage over some other CS-based imagers.

Compressive sensing-based multiple description image coding for wireless channel

Abstract: A new theory, known as Compressive Sensing (CS), has recently proved that sparse or compressible signal can be accurately reconstructed from very highly undersampled data by solving by solving a convex LI optimization problem. This paper, based on such new theory, presents a novel multiple description coding (MDC) method to combat packet loss. According to compressive sensing framework, our method has an attractive property that the reconstruction error only depends on the received measurement number but not on which measurements are received. Another advantage of our method is that it is a balanced MDC scheme with fine description granularity and low encoding complexity.

Image denoising based on translation invariant Directional Lifting

Abstract: Adaptive Directional Lifting (ADL) has been successfully implemented in image compression and denoising due to the feature of simple structure and flexible directional selectivity. However, image denoising by means of ADL introduces many visual artifacts caused by Gibbs phenomena due to the lack of translation invariance. In this paper, we propose a translation invariant directional lifting (TI-DL) by employing the cycle-spinning based technique to reduce artifacts in denoising results. Moreover, the inefficiency and high computational complexity of the orientation estimation technique in ADL strongly influences the performance. In order to achieve better denoising results, in this paper, 2-D Gabor filters are adopted for orientation estimation to achieve better orientation estimation results with lower complexity. Experimental results demonstrate that the proposed method achieves state-of-art denoising performance in terms of both objective (PSNR) and subjective (SSIM) evaluation.

Angel estimation via frequency diversity of the SIAR radar based on Bayesian theory

Abstract: The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar. The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency diversity process. As using the traditional Fourier transform will result in the target spectral with large sidelobe, the method presented in this paper firstly makes the preordering treatment for the position of the received antenna. Then, the Bayesian maximum posteriori estimation with l2-norm weighted constraint is utilized to achieve the equivalent uniform array echo. The simulations present the spectrum estimation in angle precision estimation of multiple targets under different SNRs, different virtual antenna numbers and different elevations. The estimation results confirm the advantage of SIAR radar both in array expansion and angle estimation.

Overlong instruction set microprocessing system suitable for matrix inversion

Abstract: The invention discloses an overlong instruction set microprocessing system suitable for matrix inversion, mainly solving the problems of large circuit scale and low arithmetic speed of the prior art.The system comprises four groups of arithmetic units, a global register unit, two local register units, a data address-generating unit, a program-sequencing unit and a data input/output storage unit,wherein the global register unit is connected between the data input/output storage unit and the four groups of arithmetic units for providing operands and register arithmetic results for the arithmetic units; the first local register unit (A) is connected between the first group of arithmetic units (1) and the second group of arithmetic units (2); and the second local register unit (B) is connected between the third group of arithmetic units (3) and the fourth group of arithmetic units (4) for registering middle results of the corresponding arithmetic units. The invention has the advantages of high processing speed and small circuit scale and is applicable to digital communication and digital signal processing.

Using a weighted zeroblock coder for satellite image compression

Abstract: In this paper, we propose an embedded satellite image compression method using Weighted ZeroBlock Coding (WZBC) and optimal sorting. In order to reduce average codeword length, Set Partition Embedded block (SPECK) and Embedded ZeroBlock Coder (EZBC) both encode significant block-sets with fixed-length bits, while WZBC assigns different-length bits to encode block-sets which contain different numbers of significant subblocks. In view of the context correlation among coefficients/blocks, WZBC employs a weight context to optimize the scanning order of the significance testing and the ratedistortion performance. Experimental results show that the proposed WZBC in binary coding mode provides excellent coding performance compared with those of SPECK and Set Partitioning In Hierarchical Trees (SPIHT) which use arithmetic coding, and can even closely approach that of JPEG2000. When arithmetic coding is extensively used, the proposed method has clear advantages.

Live demonstration: Spatial-temporal color video reproduction from noisy CFA sequence track: Digital signal processing

Abstract: This demonstration shows a spatial-temporal denoising and demosaicking scheme for noisy CFA videos. This scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structures. The experimental results showed that this scheme achieves promising color video reproduction in terms of both PSNR and visual perception.

Spatial-Temporal Color Video Reconstruction From

Abstract: Single-sensor digital video cameras use a color filter array (CFA) to capture video and a color demosaicking (CDM) procedure to reproduce the full color sequence. The reproduced video frames suffer from the inevitable sensor noise introduced in the video acquisition process. This paper presents a spatial-temporal denoising and demosaicking scheme that works without explicit motion estimation. We first perform patch based denoising on the mosaic CFA video. For each CFA patch to be denoised, similar patches are selected within a local spatial- temporal neighborhood. The principal component analysis is performed on the selected patches to remove noise. We then apply an initial single-frame CDM to the denoised CFA data, and subsequently post-process the demosaicked frames by exploiting the spatial-temporal redundancy to reduce the color artifacts. The experimental results on simulated and real noisy CFA sequences demonstrate that the proposed spatial-temporal CFA video de- noising and demosaicking scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structures. Index Terms—Color demosaicking (CDM), color filter array (CFA), denoising, video enhancement.

Inter frame coding with adaptive transform

Abstract: For inter frame coding, the motion-compensated residual takes a large proportion of the total bits and the efficiency of the followed transform greatly affects the compression performance. In this paper, we propose an adaptive transform scheme to further exploit the non-local correlation for the motion-compensated residual. For a video sequence, there are usually repeating similar contents, especially between adjacent frames, known as temporal redundancy. We then use these content-similar blocks of the coding block, which most probably reflect the characteristic of the coding block, to train the adaptive transform. The predicted block together with the boundary reconstructed pixels of the coding block forms the target patch and is used to guide the searching of similar blocks. By fully exploring the correlation of abundant similar blocks, the proposed scheme achieves 0.1~0.5 dB gain in term of PSNR at high bit rate over the state-of-the-art scheme. For Mobile and BQSquare, 1dB gain is obtained at high bit rate.