Head motion may unexpectedly occur during a CT scan. It thereby results in motion artifacts in a reconstructed image and may lead to a false diagnosis or a failure of diagnosis. To alleviate this motion problem, as a hardware approach, increasing the gantry rotation speed or using an immobilization device is usually considered. These approaches, however, cannot completely resolve the motion problem. Hence, motion estimation (ME) and compensation for it have been explored as a software approach instead. In this paper, adopting the latter approach, we propose a head motion correction algorithm in helical CT scanning, based on filtered backprojection (FBP). For the motion correction, we first introduce a new motion-compensated (MC) reconstruction scheme based on FBP, which is applicable to helical scanning. We then estimate the head motion parameters by using an iterative nonlinear optimization algorithm, or the L-BFGS. Note here that an objective function for the optimization is defined on reconstructed images in each iteration, which are obtained by using the proposed MC reconstruction scheme. Using the estimated motion parameters, we then obtain the final MC reconstructed image. Using numerical and physical phantom datasets along with simulated head motions, we demonstrate that the proposed algorithm can provide significantly improved quality to MC reconstructed images by alleviating motion artifacts.

Demands for in-vivo human molecular imaging with high resolution and high sensitivity in positron emission tomography (PET) require several new design formulae. A classical problem of the PET design, however, was the trade-off between sensitivity and resolution. To satisfy both requirements, the brain-body convertible PET with wobbling and zooming is proposed. The features of this new proposed system are wobble sampling for high-resolution imaging and zooming mode for high sensitivity, especially for the brain dedicated imaging. For the high resolution, wobbling with a linear interpolation and line spread function (LSF) deconvolution reconstruction algorithm was introduced. The result of the proposed system provided resolution up to 1.56 mm full width at half maximum (FWHM) in the brain mode and resulting in the detector-to-resolution ratio (DRR) was 2.47. For both brain phantom and in-vivo rat brain imaging, the proposed system demonstrated superior image quality compared with the commercial PET systems. The newly designed PET with wobbling and zooming also demonstrated the possibility of developing practically usable high-resolution human brain PET-MRI fusion system, especially for the neuroscience research.

Cardiac X-ray computed tomography (CT) imaging is still challenging due to the cardiac motion during CT scanning, which leads to the presence of motion artifacts in the reconstructed image. In response, many cardiac X-ray CT imaging algorithms have been proposed, based on motion estimation (ME) and motion compensation (MC), to improve the image quality by alleviating the motion artifacts in the reconstructed image. However, these ME/MC algorithms are mainly based on an axial scan or a low-pitch helical scan. In this paper, we propose a ME/MC-based cardiac imaging algorithm for the data set acquired from a helical scan with an ordinary pitch of around 1.0 so as to obtain the whole cardiac image within a single scan of short time without ECG gating. In the proposed algorithm, a sequence of partial angle reconstructed (PAR) images is generated by using consecutive parts of the sinogram, each of which has a small angular span. Subsequently, an initial 4-D motion vector field (MVF) is obtained using multiple pairs of conjugate PAR images. The 4-D MVF is then refined based on an image quality metric so as to improve the quality of the motion-compensated image. Finally, a time-resolved cardiac image is obtained by performing motion-compensated image reconstruction by using the refined 4-D MVF. Using digital XCAT phantom data sets and a human data set commonly obtained via a helical scan with a pitch of 1.0, we demonstrate that the proposed algorithm significantly improves the image quality by alleviating motion artifacts.

Even though the X-ray Computed Tomography (CT) scan is considered suitable for fast imaging, motion-artifact-free cardiac imaging is still an important issue, because the gantry rotation speed is not fast enough compared with the heart motion. To obtain a heart image with less motion artifacts, a motion estimation (ME) and motion compensation (MC) approach is usually adopted. In this paper, we propose an ME/MC algorithm that can estimate a nonlinear heart motion model from a sinogram with a rotation angle of less than 360°. In this algorithm, we first assume the heart motion to be nonrigid but linear, and thereby estimate an initial 4-D motion vector field (MVF) during a half rotation by using conjugate partial angle reconstructed images, as in our previous ME/MC algorithm. We then refine the MVF to determine a more accurate nonlinear MVF by maximizing the information potential of a motion-compensated image. Finally, MC is performed by incorporating the determined MVF into the image reconstruction process, and a time-resolved heart image is obtained. By using a numerical phantom, a physical cardiac phantom, and an animal data set, we demonstrate that the proposed algorithm can noticeably improve the image quality by reducing motion artifacts throughout the image.

In this letter, we propose a novel automatic algorithm for road extraction from remote sensing images. The algorithm includes low- and high-level processing. In the low-level processing, we determine a normalized second derivative map of road profiles of a generalized bar shape, which is width invariant and contrast proportional, and accordingly obtain initial road center pixels. In the high-level processing, using the map and initial center pixels, we initially determine road segments. The segments are then locally refined using their orientation randomness and length-to-width ratio and further refined via global graph-cut optimization. A final road network is thereby extracted in a robust manner. Experimental results demonstrate that the proposed algorithm provides noticeably more robust and higher road extraction performance in various images compared with the existing algorithms.

Block-based video-coding standards produce unwanted spatial and temporal artifacts in reconstructed videos. Among them, ringing and mosquito artifacts arise due to the quantization of high-frequency discrete cosine transform coefficients. Most of the existing artifact-reduction algorithms assume that coding information such as a standard quantization table and the corresponding quantization parameter for each block are available. In many multimedia applications, however, external video inputs are usually supplied without coding information. To effectively reduce the ringing and mosquito artifacts in a decoded input video sequence, it is necessary to control the filter strength block by block. In this paper, we present a blind block-based method to estimate the quantization amount and propose a novel post-processing algorithm based on the visibility of artifacts in terms of the human visual system using the estimated quantization amount. Experimental results demonstrate that the proposed algorithm better alleviates ringing and mosquito artifacts in various coded videos compared with the existing algorithms.

This paper presents a new framework for motion compensated frame rate up conversion (FRUC) based on variational image fusion. The proposed algorithm consists of two steps: 1) generation of multiple intermediate interpolated frames and 2) fusion of those intermediate frames. In the first step, we determine four different sets of the motion vector field using four neighboring frames. We then generate intermediate interpolated frames corresponding to the determined four sets of the motion vector field, respectively. Multiple sets of the motion vector field are used to solve the occlusion problem in motion estimation. In the second step, the four intermediate interpolated frames are fused into a single frame via a variational image fusion process. For effective fusion, we determine fusion weights for each intermediate interpolated frame by minimizing the energy, which consists of a weighted-L1-norm based data energy and gradient-driven smoothness energy. Experimental results demonstrate that the proposed algorithm improves the performance of FRUC compared with the existing algorithms.

Scatter correction is very important in 3-D PET reconstruction due to a large scatter contribution in measurements. Currently, one of the most popular methods is the so-called single scatter simulation (SSS), which considers single Compton scattering contributions from many randomly distributed scatter points. The SSS enables a fast calculation of scattering with a relatively high accuracy; however, the accuracy of SSS is dependent on the accuracy of tail fitting to find a correct scaling factor, which is often difficult in low photon count measurements. To overcome this drawback as well as to improve accuracy of scatter estimation by incorporating multiple scattering contribution, we propose a multiple scatter simulation (MSS) based on a simplified Monte Carlo (MC) simulation that considers photon migration and interactions due to photoelectric absorption and Compton scattering. Unlike the SSS, the MSS calculates a scaling factor by comparing simulated prompt data with the measured data in the whole volume, which enables a more robust estimation of a scaling factor. Even though the proposed MSS is based on MC, a significant acceleration of the computational time is possible by using a virtual detector array with a larger pitch by exploiting that the scatter distribution varies slowly in spatial domain. Furthermore, our MSS implementation is nicely fit to a parallel implementation using graphic processor unit (GPU). In particular, we exploit a hybrid CPU-GPU technique using the open multiprocessing and the compute unified device architecture, which results in 128.3 times faster than using a single CPU. Overall, the computational time of MSS is 9.4 s for a high-resolution research tomograph (HRRT) system. The performance of the proposed MSS is validated through actual experiments using an HRRT.

Block-based coding introduces an undesirable discontinuity between neighboring blocks in reconstructed images. This image degradation, referred to as blocking artifacts, arises mainly due to the loss of inter-block correlation in the quantization process of discrete cosine transform coefficients. In many multimedia broadcasting applications, such as a television, decoded video sequences suffer from blocking artifacts. In this paper, we present a novel post-processing algorithm based on increment of inter-block correlation aimed at reducing blocking artifacts. We first smooth the three lowest frequency discrete cosine transform (DCT) coefficients between neighboring blocks, in order to reduce blocking artifacts in the flat region, which are most sensitive to the human visual system. We then group each edge block and its matched blocks together and apply group-based filtering to increase the correlation between grouped blocks. This suppresses blocking artifacts in the edge region while preserving details. In addition, the algorithm is extended to reduce flickering artifacts as well as blocking artifacts in video sequences. Experimental results show that the proposed method successfully alleviates blocking artifacts in both images and videos coded with low bit-rates.

A digital elevation model (DEM) contains the elevation of terrain, which is widely used in the geographic information system (GIS). However, a DEM may not be accurate due to the measurement inaccuracy and the change of topography with time. In this paper, we propose a feature based algorithm for detecting DEM elevation errors using a camera image. In the algorithm, we first extract ridges in a depth map obtained from a DEM. We then extract ridges in the corresponding camera image, and determine the elevation error in the DEM, if any, by comparing the corresponding points of ridges between the depth map and camera image. To robustly extract a ridge in the camera image, we adopt a mask that is obtained using the ridge information in the depth map, and apply dynamic programming within the mask. Experimental results show that the proposed algorithm can reliably determine elevation errors in the DEM.

In example-based super-resolution, it is difficult to determine appropriate high-frequency (HF) patches from a training database by using only the information of one input image. In this letter, we utilize the sharpness of high-resolution (HR) patch candidates for the reliable determination of HF patches. For each input patch, we first preselect a sufficient number of HF patch candidates and produce HR patches by adding the candidates to the input patch. After removing the outlier patches, we then reselect several HF patches according to the patch characteristic for producing the final HR image. This reselection procedure is optimized for edge patches and non-edge patches, respectively. Experimental results show that the proposed algorithm provides sharper details compared to the existing algorithms.

Poisson Image Editing(PIE) is one of the most well known methods for seamless image interpolation. However, direct application of PIE to large scale images(e.g. satellite image with billions of pixels) can result in hours of operation time. We propose a modification of PIE method, which enables the original method to be performed in smaller blocks, largely improving the operation time. The key idea of Block Poisson Method(BPM) is to prepare the boundary conditions for each block and perform PIE separately. Our algorithm can be effectively applied to satellite image stitching, especially for cases where the satellite images are significantly degraded at detectors' edge. Resulting images show that BPM performs fairly the same as the original PIE with considerably reduced operation time.

The interest for the registration between 2D ultrasound (US) and preoperative CT or MR images has been growing for US-guided diagnosis, intervention, and surgery. In our previous study, we proposed a real-time and automatic registration system between two images of the liver without any help of positioning sensors. We have confirmed that the system can provide an accurate and reliable registration performance, if sufficient features are included in a current US image. In this paper, we propose a robust position estimation system of a moving liver lesion based on the previously proposed registration system. The system indirectly but reliably estimates the lesion position by registering a US image including sufficient features to 4D MR images, even if the US image does not include the target lesion. The proposed system is evaluated on three clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that the registration error for target lesions is less than 5 mm on average, which is considered acceptable for many clinical applications.

In this paper, we propose a novel pixel-level multi-sensor image fusion algorithm with simultaneous contrast enhancement. In order to accomplish both image fusion and contrast enhancement simultaneously, we suggest a modified framework of the subband-decomposed multiscale retinex (SDMSR), our previous contrast enhancement algorithm. This framework is based on a fusion strategy that reflects the multiscale characteristics of the SDMSR well. We first apply two complementary intensity transfer functions to source images in order to effectively utilize hidden information in both shadows and highlights in the fusion process. We then decompose retinex outputs into nearly nonoverlapping spectral subbands. The decomposed retinex outputs are then fused subband-by-subband, by using global weighting as well as local weighting to overcome the limitations of the pixel-based fusion approach. After the fusion process, we apply a space-varying subband gain to each fused subband-decomposed retinex output according to the subband characteristic so that the contrast of the fused image can be effectively enhanced. In addition, in order to effectively manage artifacts and noise, we make the degree of enhancement of fused details adjustable by improving a detail adjustment function. From experiments with various multi-sensor image pairs, the results clearly demonstrate that even if source images have poor contrast, the proposed algorithm makes it possible to generate a fused image with highly enhanced contrast while preserving visually salient information contained in the source images.

Since current coding standards rely on block based processing, reconstructed images include horizontal and vertical grid noise along the block boundaries. This image degradation, called blocking artifacts, is mainly caused due to the quantization process of discrete cosine transform (DCT) coefficients. In this paper, we present a novel post processing framework for blocking artifact reduction, which is based on the correction of a few quantized DCT coefficients. We first smooth inter-block DCT coefficients for the three lowest frequency (LF) ones, in order to reduce blocking artifacts in the flat region which are most sensitive to the human visual system (HVS). We then apply block based 3-D filtering to the edge region to reduce the remaining artifacts. Experimental results show that the proposed method successfully alleviates blocking artifacts in the images coded with low bit-rates.

In this letter, we propose a novel contrast enhancement algorithm for optical remote sensing satellite and aerial images. The algorithm is based on multiscale retinex (MSR). Unlike the MSR, however, we first decompose retinex outputs into nearly nonoverlapping spectral subbands. We then apply a space-varying subband gain to each subband-decomposed retinex output by considering its subband characteristic so that the image contrast can be effectively enhanced. In order to improve details in both shadows and highlights, we propose a new hybrid intensity transfer function based on logarithmic functions. In addition, we make the degree of enhancement of details adjustable by introducing a detail adjustment function based on a local standard deviation map of the original image. Experimental results clearly demonstrate that the proposed algorithm effectively distinguishes details in the whole image automatically and greatly enhances the overall contrast in various images.

This letter presents a new framework for improving the spatial resolution of infrared (IR) images based on the high-resolution visible image information. Edge regions in an IR image, which have a strong correlation with the aligned edges in the visible image, are sharpened by using their high frequency patches, which are locally estimated from the visible image. The estimation is performed on the basis of intensity correlations between two images. In addition, in order to improve the resolution in the uncorrelated edge regions and the texture regions where visible image information is not available, we adopt learning-based and reconstruction-based super resolution algorithms, respectively. Experimental results demonstrate that the proposed algorithm improves the spatial resolution compared with the existing upsampling algorithms.

In color image processing, an image is usually processed in a color space consisting of luminance and chrominance components. The processed result is then converted to the RGB color space for display. However, a straightforward color space conversion can cause color distortion due to gamut difference between the two color spaces. In order to alleviate this gamut problem, we propose two gamut-adaptive correction schemes. For the chrominance component processing, we propose the gamut-adaptive clipping scheme to effectively solve the out-of-gamut problem. For the luminance component processing of a color image, we propose the gamut-adaptive scaling scheme to alleviate both the out-of-gamut and under-saturation problems. The proposed methods can be applied for image processing in any color space since the proposed correction is performed in the RGB color space. Experimental results show that the proposed methods can preserve the luminance and hue values and provide rich colors by adaptively controlling the saturation value according to the luminance component value.

Since the visual quality of an infrared (IR) image is usually unsatisfactory due to blurred edges and lack of textures, it is sometimes hard to obtain sufficient information from the IR image. In this paper, we present a novel framework for the IR image enhancement with the help of its aligned high resolution visible image. In the algorithm, we first prepare an aligned pair of IR and visible images through multi-sensor image registration. We then define a weight map based on edge correspondence in order to properly transfer the sharp edge property in the visible image to the IR image while avoiding unwanted blurring and distortion. We enhance the IR edges with high weights by applying visible-image-driven anisotropic diffusion with adaptive diffusion parameters. Finally, we deblur the remaining area to obtain a result that is enhanced uniformly over the whole IR image. Experimental results show that the proposed method can provide quality-improved IR images.

Learning-based super-resolution algorithms synthesize a high-resolution image based on learning patch pairs of low- and high-resolution images. However, since a low-resolution patch is usually mapped to multiple high-resolution patches, unwanted artifacts or blurring can appear in super-resolved images. In this paper, we propose a novel approach to generate a high quality, high-resolution image without introducing noticeable artifacts. Introducing robust statistics to a learning-based super-resolution, we efficiently reject outliers which cause artifacts. Global and local constraints are also applied to produce a more reliable high-resolution image. Experimental results demonstrate that the proposed algorithm can synthesize higher quality, higher-resolution images compared to the existing algorithms.

In this paper, we propose a novel framework for motion compensated frame rate up conversion. The proposed algorithm consists of two steps: generation of interpolated images and fusion of them. In the first step, we generate four different interpolated frames between the given two frames. Those frames are obtained through motion compensated interpolation, by using a different set of optical flow fields that are estimated from the four consecutive frames. In the second step, we fuse the four interpolated images into one by using a variational structure so that we can effectively remove outliers appearing due to false optical flows in occlusion regions. Experimental results demonstrate that the proposed algorithm improves objective and subjective visual qualities compared to the existing algorithms.

This letter presents a robust similarity measure for registering charged-couple device (CCD) and infrared (IR) images. The measure is based on the entropy obtained from a 3-D joint histogram incorporating edginess and modified generalized gradient vector flow (GGVF). To make a reliable mapping relationship between the edge regions of two images, the concept of edginess was adopted so registration performance would be affected mainly by gradient existences rather than their magnitudes. In addition, by adopting the GGVF, we relaxed a narrow capture range problem in conventional gradient-based measures. Experimental results showed that the proposed measure performs more robustly than existing measures.

A recently introduced optical flow based deinterlacing algorithm showed a promising performance for the sequences with well estimated optical flows. However, estimated optical flows mostly include some errors, which cause significant artifacts. To reduce such artifacts, we propose an improved algorithm by considering flow vector reliability in terms of motion linearity, motion uniqueness, and consistent motion in a flat region. Moreover, to maximize the resolution of deinterlaced images, we propose intensity reliability which utilizes the field parity information. Experimental results show that the proposed algorithm noticeably improves the quality of deinterlaced images.

This paper presents a novel multi-sensor image fusion algorithm to generate a contrast-enhanced fused image. The algorithm is based on a framework of the subband decomposed multiscale retinex (SD-MSR) that is one of the contrast enhancement methods. It also uses two complementary intensity transfer functions to enhance the details in both shadows and highlights. For effective contrast enhancement, we suggest a novel fusion strategy which reflects the characteristics of SD-MSR well. Thereby, even if input images have poor contrast, the proposed algorithm is able to generate a fused image with highly enhanced contrast. Experimental results demonstrate that the algorithm performs well for generating high contrast fused images.

Learning-based super-resolution algorithms synthesize high-resolution details by using training data. However, since an input image does not belong to a training image set, there is a limitation in recovering its high-frequency details. In our approach, we build and utilize residual training data to complement missing details. We first estimate a pair of mid- and high-frequency images of each training image by using ordinary training data. We then build residual training data by obtaining the residual mid-and high-frequency images that denote the difference between the estimation and original. Thereby, we can synthesize high-resolution details better by using both ordinary and residual training data sets. In addition, in order to use training data more efficiently, we adaptively select low-resolution patches in an input image. Experimental results demonstrate that the proposed method can synthesize higher-resolution images compared to the existing algorithms.

It is clinically important to quantify the geometric parameters of an abnormal vessel, as this information can aid radiologists in choosing appropriate treatments or apparatuses. Centerline and cross-sectional diameters are commonly used to characterize the morphology of vessel in various clinical applications. Due to the existence of stenosis or aneurysm, the associated vessel centerline is unable to truly portray the original, healthy vessel shape and may result in inaccurate quantitative measurement. To remedy such a problem, a novel method using an active tube model is proposed. In the method, a smoothened centerline is determined as the axis of a deformable tube model that is registered onto the vessel lumen. Three types of regions, normal, stenotic, and aneurysmal regions, are defined to classify the vessel segment under-analyzed by use of the algorithm of a cross-sectional-based distance field. The registration process used on the tube model is governed by different region-adaptive energy functionals associated with the classified vessel regions. The proposed algorithm is validated on the 3-D computer-generated phantoms and 3-D rotational digital subtraction angiography (DSA) datasets. Experimental results show that the deformed centerline provides better vessel quantification results compared with the original centerline. It is also shown that the registered model is useful for measuring the volume of aneurysmal regions.

In this paper, we propose a novel image enhancement algorithm by improving the standard multiscale retinex (MSR) algorithm. A modified retinex output using a combination of two logarithmic functions enhances the details in highlights as well as the details in shadows. Sub-band decomposition of the modified retinex output makes the individual gain control of spectral bands possible, so that we can enhance image details according to band characteristics. Due to the effective sub-band based gain control, the final enhanced image can be automatically obtained without a user-assisted "canonical" gain/offset control. Experimental results clearly demonstrate that the proposed algorithm effectively distinguishes the details in both highlights and shadows and greatly enhances the overall contrast while maintaining a natural scene.

In multi-sensor image fusion, multi-resolution approaches became popular because they can preserve detailed information well. Among them, the gradient-based multi-resolution (GBMR) algorithm is known to effectively reduce ringing artifacts near edges compared with the discrete wavelet transform (DWT)-based algorithm. However, since the GBMR algorithm does not consider the diagonal direction, the ringing artifacts reduction is not satisfactory at diagonal edges. In this paper, we generalize the GBMR algorithm by adopting the wavelet structure. Thereby, the proposed algorithm improves the fusion process in high-frequency sub-bands so as to preserve details of input images. Meanwhile, the algorithm fuses the low-frequency sub-band by considering the overall contrast in the output image. To evaluate the proposed algorithm, we compare it with the DWT-based and GBMR algorithms. Experimental results clearly demonstrate that the proposed algorithm effectively reduces ringing artifacts for edges of all directions and greatly enhances the overall contrast while minimizing visual information loss.

Super-resolution is the process of combining multiple low-resolution images to produce a higher resolution image. Because the super-resolution problem is an ill-posed one, many regularized algorithms have been proposed. Those algorithms usually use the same regularization term for a whole image. However, since an image generally contains various regions of different characteristics, simple regularization is not good enough for the prospective result. In this paper, we propose a region-based super-resolution algorithm to apply a suitable regularization term for each region. In the algorithm, the image is divided into homogeneous and inhomogeneous regions. According to the type of region, we apply different filters for regularization. Regularization parameters are also adaptively determined during the iteration. Simulation results show that the proposed algorithm is superior to the conventional algorithm in terms of objective quality as well as subjective one.

Wavelet transform based image coding algorithms have the two inherent scalable features of SNR scalability and spatial (or resolution) scalability. However, fine scalability is supported only for the SNR, but spatial scalability is usually restricted to scales of powers of two which may not be considered fine. In this paper, we introduce the fine spatial scalability to wavelet transform based coding systems, by examining the relationship between the subbands of wavelet packet and the original signal spectrum. The proposed fine spatial scalability is useful for effective image resizing and efficient image data transmission according to the display resolution.

This paper presents a fast full-search algorithm (FSA) for rate-constrained motion estimation. The proposed algorithm, which is based on the block sum pyramid frame structure, successively eliminates unnecessary search positions according to rate-constrained criterion. This algorithm provides the identical estimation performance to a conventional FSA having rate constraint, while achieving considerable reduction in computation.

Effective shape coding in MPEG-4 requires a motion estimation (ME) procedure, which is a great burden for real-time encoders. This paper deals with fast ME for MPEG-4 shape coding. The proposed algorithm utilizes inherent properties of shape coding, i.e., coding based on motion vector predictor for shape (MVPS) and motion vector (MV) correlation between neighboring binary alpha blocks (BABs). By properly using these properties, the proposed algorithm can reduce a processing speed. In addition, the proposed algorithm can be combined with any conventional fast block matching algorithm (BMA) being used in texture ME and a bit-packing technique based on binary feature of shape information. Simulation results show that the proposed algorithm can reduce the computational complexity of ME for shape coding to 68.3% even in the worst case. Also, by simply combining the proposed algorithm with a fast BMA and a bit-packing technique, we can achieve the complexity suitable for real-time software implementation of MPEG-4 shape encoding.

Existing fast block motion estimation algorithms, which try to reduce the number of search points, utilize the motion vector (MV) characteristics of high spatial correlation as well as center-biased distribution, in predicting an initial MV. Even though they provide good performance for slow motion sequences, they suffer from poor accuracy for fast or complex motion sequences. We propose a new fast and efficient block motion estimation algorithm. The proposed algorithm utilizes a new predictor obtained from one-dimensional feature matching based on selective integral projections. This low complexity procedure enables the selection of a better initial search point so that a simple gradient descent search near this point may be enough to find the global minimum point. Compared with previous fast search algorithms, the proposed algorithm has a lower computational complexity and provides better prediction performance, especially for fast or complex motion sequences.

Vector quantization for image compression requires expensive encoding time to find the closest codeword to the input vector. This paper presents a fast algorithm to speed up the closest codeword search process in vector quantization encoding. By using an appropriate topological structure of the codebook, we first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that with little preprocessing and memory cost, the proposed search algorithm significantly reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms.

A fast block-matching motion estimation algorithm is proposed for efficient hardware implementation in video coding standards. The proposed algorithm consists of the binary block-matching (BBM) step based on edge information, the first refinement step around the motion vector (MV) obtained from BBM, and the second but conditional refinement step using spatial correlation of neighboring MVs. The proposed algorithm gives the PSNR performance similar to that of the full search. Moreover, due to BBM and the fact that the refinement steps and the half-pel search perform the same operation, its hardware implementation can be performed with several prospective features such as small number of gates, low power consumption, and short critical path delay, etc.

This paper describes an algorithm of semi-automatic video segmentation for object tracking. In this semi-automatic method, a user specifies an object of interest in the first frame; then the specified object is to be tracked in the remaining frames. The proposed tracking algorithm consists of two steps, ie, object prediction and boundary refinement. For object prediction, we propose two novel object prediction methods based on motion estimation and color, respectively. By using these two object prediction schemes, pixels are assigned either to the object or background. Then, if some pixels are judged differently by these two schemes, they are considered as uncertain areas. By refining these uncertain areas, we can obtain object boundaries closer to the real ones compared to conventional algorithms. Simulation results show that the proposed method provides efficient tracking results for various video sequences.

Vector quantization for image compression requires expensive encoding time to find the closest codeword to the input vector. This paper presents a fast algorithm to speed up the closest codeword search process in vector quantization encoding. By using an appropriate topological structure of the codebook, we first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that with little preprocessing and memory cost, the proposed search algorithm significantly reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms.

This paper proposes a scheme to produce realistic force reflection in a needle insertion problem. The target system is a spine needle biopsy simulator for tumor inspection by needle insertion. Simulated force is calculated using a 3D human tissue model and the orientation and position of the needle, and is produced through the PHANToM/sup TM/ device. To generate realistic force reflection, the directional force of the needle has been generated by a related tissue model, and the rotational force is generated using a pivot to keep the needle in the initial inserted direction after puncturing the skin. Since the haptic device has limitations in generating high stiffness and large damping, a scale-down model and digital filter are used to stabilize the system.

Shows how to implement haptic rendering for a needle insertion problem with a volume graphic model. The target is a spine biopsy simulator for tumor inspection by needle insertion. Simulated force is calculated from the relationship between voxel data and the orientation and position of the needle. At first, it is generated using PHANToM/sup TM./ The needle puncturing resistive force is calculated from a stiffness model and the correction force to give physical constraint to the needle is derived using a pivot. For more realistic force feedback voxel based modeling is considered. MRI T1 value and CT density can be used to give physical properties to voxel. Soft tissue can be modeled as a damper using MRI value in the needle insertion problem, and the stiffness of hard tissue is derived from CT density. Skin deformation is modeled with Kelvin's viscoelastic model. Since the high stiffness and large damping need to be simulated, it is attempted to generate force using active actuators and passive devices together.

We propose a fast multi-resolution block-matching algorithm (BMA) using multiple motion vector (MV) candidates and spatial correlation in MV fields, called a multi-resolution motion search algorithm (MRMCS). The proposed MRMCS satisfies high estimation performance and efficient LSI implementation. This paper describes the MRMCS with three resolution levels. At the coarsest level, two MV candidates are obtained on the basis of minimum matching error for the next search level. At the middle level, the two candidates selected at the coarsest level and the other one based on spatial MV correlation at the finest level are used as center points for local searches, and a MV candidate is chosen for the next search level. Then, at the finest level, the final MV is obtained from local search around the single candidate obtained at the middle level. This paper also describes an efficient LSI architecture based on the proposed algorithm for low bit-rate video coding. Since this architecture requires a small number of processing elements (PEs) and a small size on-chip memory, MRMCS can be implemented with a much smaller number of gates than other conventional architectures for full-search BMA while keeping a negligible degradation of coding performance. Moreover, the proposed motion estimator can support an advanced prediction mode (8/spl times/8 prediction mode) for H.263 and MPEG-4 video encoding. We implement this architecture with about 25 K gates and 288 bytes of RAM for a search range of [-16.0, +15.5] by using a synthesizable VHDL.

Most of the content-based image retrieval systems require a distance computation for each candidate image in the database. As a brute-force approach, the exhaustive search can be employed for this computation. However, this exhaustive search is time-consuming and limits the usefulness of such systems. Thus, there is a growing demand for a fast algorithm which provides the same retrieval results as the exhaustive search. We propose a fast search algorithm based on a multiresolution data structure. The proposed algorithm computes the lower bound of distance at each level and compares it with the latest minimum distance, starting from the low-resolution level. Once it is larger than the latest minimum distance, we can remove the candidates without calculating the full-resolution distance. By doing this, we can dramatically reduce the total computational complexity. It is noticeable that the proposed fast algorithm provides not only the same retrieval results as the exhaustive search, but also a faster searching ability than existing fast algorithms. For additional performance improvement, we can easily combine the proposed algorithm with existing tree-based algorithms. The algorithm can also be used for the fast matching of various features such as luminance histograms, edge histograms, and local binary partition textures.

In this paper, we try to improve a video object-tracking algorithm using a motion re-estimation scheme. The usual object tracking algorithm is composed of two steps: object projection and boundary refinement. Since a single motion model cannot represent the movement of a semantic object, it is not precise enough to refine the object boundary. Therefore, we segment the video object by an efficient segmentation algorithm. If errors are large in some regions, they are selected for re-estimating motion models. By projecting those regions using new estimated motion models, we can obtain more reliable object projection, which can produce accurate tracking results. The simulation results show that the tracked boundary of the video object having complicated motion is improved.

This paper proposes a fast motion estimation algorithm for MPEG-4 shape coding. The proposed algorithm is based on inherent properties of shape coding as well as shape information, i.e., context-based motion estimation feature, motion vector (MV) correlation between neighboring binary alpha blocks (BABs), and THE binary feature of shape information. By using these properties, the proposed algorithm can achieve a high processing speed. Simulation results show that our algorithm can reduce the computational complexity of ME for shape coding up to 90% at least even for the worst case. Therefore, the algorithm is suitable for real-time software applications of MPEG-4 shape encoding.

We present a novel multi-resolution block matching algorithm (BMA) for fast motion estimation. At the coarsest level, a full search BMA (FSBMA) is performed for searching complex or random motion. Concurrently, spatial correlation of motion vector (MV) field is used for searching continuous motion. Here we present an efficient method for searching full resolution MVs without MV decimation even at the coarsest level. After the coarsest level search, two or three initial MV candidates are chosen for the next level. At the further levels, the MV candidates are refined within much smaller search areas. Simulation results show that in comparison with FSBMA, the proposed BMA achieves a speed-up factor over 710 with minor PSNR degradation of 0.2 dB at most, under a normal MPEG-2 coding environment. Furthermore, our scheme is also suitable for hardware implementation due to regular data-flow.

This study aims at finding an efficient compensation method of transmission errors in low bit-rate mobile communications using the H.263 video codec. For this aim, we suggest a novel error compensation method in the encoder (or transmitter), that can remove visual quality degradation due to spatio temporal error propagation by utilizing a feedback channel. In the proposed method, a corrupted-group of blocks (GOB) is concealed to avoid annoying artifacts due to the bit-error, and the GOB and its corresponding frame number are reported to the encoder via the feedback channel. Then, the encoder evaluates the negative acknowledgments and reconstructs the spatial and temporal error propagation by using a novel fast redecoding algorithm. The proposed error compensation method is compared with the two existing ones: the reference picture selection method and the error tracking method. Experimental results show that the proposed method is superior to the existing ones in many aspects.

A traditional method for down conversion of MPEG compressed video performs low-pass filtering and subsampling after full decompression. However, the method has two shortcomings: large memory requirement and high computational complexity. To overcome the drawbacks, recent research has focussed on down conversion in the DCT domain. Existing DCT domain methods need only a quarter of full frame memory because completely reduced frames are stored in the frame memory. But the loss of field information often causes significant error during motion compensation. To solve this problem, we adopt a hybrid structure where field information is preserved by performing down conversion in the DCT domain only for horizontal direction and down conversion in the spatial domain for vertical direction. Furthermore, we present a novel technique for the horizontal down conversion which adopts a modified 4-IDCT kernel. The modified 4-IDCT kernel outperforms the conventional 4-IDCT kernel in that it performs low-pass filtering and sub-sampling additionally. Experimental results show that the proposed scheme provides high visual quality while maintaining comparable complexity.

Volume rendering is a powerful tool for visualizing sampled scalar values from 3D data without fitting geometric primitives to the data. However, the size of volume data is usually too big to handle in real time. Recently, various volume rendering algorithms have been proposed in order to reduce the rendering time. However, most of the proposed algorithms are not proper for large volume data, which is required in some applications such as the spine biopsy simulator. In this paper, we propose a block-based fast volume rendering algorithm using shear-warp factorization. The algorithm performs volume rendering by using information of organ segmentation as well as 3D volume data, which is a common environment in the spine biopsy simulator. The proposed block-based algorithm can reduce storage requirement and increase the rendering speed by treating the 3D data on a block-by-block basis. The proposed algorithm is evaluated by rendering 3D body images obtained from X-ray CT (computed tomograph) system.

We propose a new hierarchical motion estimator architecture that supports the advanced prediction mode of recent low bit-rate video coders such as H.263 and MPEG-4. In the proposed VLSI architecture, a basic searching unit (BSU) is commonly utilized for all hierarchical levels to make a systematic and small sized motion estimator. Also, since the memory bank of the proposed architecture provides a scheduled data flow for calculating the 8×8 block-based sum of absolute difference (SAD), both a macroblock-based motion vector (MV) and four block-based MVs per macroblock can be simultaneously obtained for the advanced prediction mode. Even with its small size of implementation, the proposed motion estimator provides similar coding performance as that of the full search block matching algorithm (FSBMA).

This paper describes a semi-automatic algorithm for semantic video object tracking. The proposed algorithm consists of three steps: object boundary projection, uncertain area extraction, and boundary refinement. The object boundary of the previous frame is projected on the current frame by using the object-based motion compensation. Then, uncertain area is determined via two modules: MC error-test and color similarity test. Finally, the exact object boundary of the current frame is obtained from extracted uncertain area by boundary refinement using color information of the current frame. The proposed video object extraction method provides efficient tracking results for various video sequences by using only two consecutive frames.

This paper presents a method to remove blocking artifacts in low bit-rate block-based video coding. The proposed algorithm has two separate filtering modes, which are selected by pixel behavior around the block boundary. In each mode, proper one-dimensional filtering operations are performed across the block boundary along the horizontal and vertical directions, respectively. In the first mode, corresponding to flat regions, a strong filter is applied inside the block as well as on the block boundary because the flat regions are more sensitive to the human visual system (HVS) and the artifacts propagated from the previous frame due to motion compensation are distributed inside the block. In the second mode, corresponding to other regions, a sophisticated smoothing filter which is based on the frequency information around block boundaries, is used to reduce blocking artifacts adaptively without introducing undesired blur. Even though the proposed deblocking filter is quite simple, it improves both subjective and objective image quality for various image features.

Motion vector coding efficiency is becoming an important issue in low bitrate video coding because of its increasing relative bit portion. This work presents a new motion vector coding technique based on minimum bitrate prediction. In the proposed scheme, a predicted motion vector is chosen from the three causal neighboring motion vectors so that it can produce a minimum bitrate in motion vector difference coding. Then the prediction error, or motion vector difference (MVD), and the mode information (MODE) for determining the predicted motion vector at a decoder are coded and transmitted in order. Sending bits for the MVD ahead of bits for the MODE, the scheme can minimize the bit amount for the MODE by taking advantage of the fact that the minimum bitrate predictor is used for motion vector prediction. Adaptively combining this minimum bitrate prediction scheme with the conventional model-based prediction scheme, more efficient motion vector coding can be achieved. The proposed scheme improves the coding efficiency noticeably for various video sequences.

We try to improve transform coding efficiency by alleviating the interblock correlation due to the small size of the block. The proposed method needs minor modification from conventional transform coding techniques such as JPEG, and reduces the information loss in the coding procedure for a given bit rate. Simulation results demonstrate that the method drastically diminishes the blocking effects and enhances the subjective visual quality compared with such existing algorithms as JPEG and LOT.

The Kohonen's self-organizing map algorithm for vector quantization of images is modified to reduce the edge degradation in the coded image. The learning procedure is performed by adaptive learning rates that are determined according to the image block activity. The simulation result of 4x4 vector quantization for 512x512 image coding demonstrates the feasibility of the proposed method.