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Pattern Matching is the most conventional method of binary text image compression that has been only used in the 2-D domain of textual image signals. In this paper a pattern matching technique is proposed in the 1-D domain of chain code description signal of printed binary textual Farsi-Arabic images. In printed Farsi-Arabic scripts, contrary to latin scripts, letters usually attach to each other...
To reach the rural masses, banks are going all out in providing a user-friendly banking experience. To boost micro financing initiatives, banks are deploying biometric solutions with ATMs. Establishing the identity of a rural depositor through biometrics makes it possible for illiterate or barely literate folks to become part of the banking user community. Fingerprint recognition has been recognized...
In this paper we investigate the prediction scheme of Context Based Adaptive Lossless Image Coding (CALIC), the standard for lossless/near lossless image compression for continuous-tone finger-print images. We show that it is not sufficient to consider the prediction technique in a single direction for a fingerprint image as a whole for Gradient Adjusted Predictor (GAP). As a result, we propose an...
Generally on CCD Bayer CFA images, compression is performed after demosaicing. Nowadays, for better image quality compression-first schemes are preferred over the conventional demosaicing-first schemes. In some high-end photography applications, original CFA images are required; in such cases lossless compression of CFA images is necessary. A fair performance is obtained for CFA images by lossless...
In this paper, we propose a steganalysis algorithm to detect spatial domain least significant bit (LSB) matching steganography, which is much harder than the detection of LSB replacement. We use features based on histogram of run length and histogram characteristic function to detect the LSB Matching. Experimental results on two datasets demonstrate that this method has superior results compared with...
H.264 adopts variable length coding and interprediction techniques, so compressed image is very sensitive to channel error. Meanwhile errors will influence subsequent frames, easily leading to the distortion of rebuilding images. To enhance the quality of reconstructed images, this text proposes a weighted outer boundary matching algorithms based on multiple reference frames, which exploits the characteristic...
A new approach for highly robust and precise global motion estimation (GME) using motion vectors (MVs) is presented. We show that this approach obtains precise higher-order short-term motion parameters for global motion using motion vectors solely. The approach is general and works for different mathematical methods including least-squares and Newton-Raphson method. We show that the approach is suitable...
H.264/AVC is the newest one among several video compression standards. The main goals of H.264/AVC are to achieve efficient compression performance and a network friendly video coding. However, if an error occurs when transmitting compressed video, error concealment is needed to prevent error propagation and to improve the video quality. In this paper, we propose the temporal error concealment algorithm...
Motion estimation is an important component for video processing and compression. A fast spatiotemporal statistical information based motion estimation technique is proposed in this paper. It uses the spatiotemporal correlation in the image sequence to detect and to estimate global motion, based which a block matching approach is applied for more accurate motion estimation. The experimental results...
Due to channel noise or congestion, video data packets can be lost when transmit in error-prone networks. In addition, compressed video sequences are very fragile to transmission errors. Most of inter-frame error concealment (EC) methods estimate one motion vector (MV) for a corrupted macroblock (MB) or sub-partition. This may result in part of the boundary of the reconstruction MB or subpartition...
In this paper, we introduce a new predictive image compression scheme that compresses an image by a set of parameters computed for individual blocks of different types. These parameters include the average and difference of the representative intensities of an image block, together with the index of a pattern associated with the block visual activity. The block representative gray values are computed...
In this paper, a new variable block-size image compression scheme is presented. A quadtree segmentation is employed to generate blocks of variable size according to their visual activity. Inactive blocks are coded by the block mean, while active blocks are coded by the proposed matching algorithm using a set of parameters associated with the pattern appearing inside the block. Both the segmentation...
We proposed a compressed sensing Super Resolution algorithm based on wavelet. The proposed algorithm performs well with a smaller quantity of training image patches and outputs images with satisfactory subjective quality. It is tested on classical images commonly adopted by Super Resolution researchers with both generic and specialized training sets for comparison with other popular commercial software...
Traditionally, the problems of applying orthogonal matching pursuit (OMP) to large images are its high computing time and its requirement for a large matrix. In this paper, we propose a fast image recovery algorithm by dividing the image into block of n??n pixels and applying OMP to each n??n block instead of the entire image. The key idea is that small matrix requires less computing time and less...
In this paper, we explore the key factors in the design and implementation of visual computing (image processing and computer vision) algorithms on the massive parallel GPU (graphics processing units). The goal of the exploration is to provide common perspective and guidelines of using GPU for visual computing applications. We have selected three nontrivial applications (multiview stereo matching,...
This paper addresses the super-resolution problem for low quality cartoon videos widely distributed on the web, which are generated by downsampling and compression from the sources. To effectively eliminate the compression artifacts and meanwhile preserve the visually salient primitive components (e.g., edges, ridges and corners), we propose an adaptive regularization method depending on the degradation...
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., Gaussian or random Fourier matrices). For a given m, perfect reconstruction usually requires high complexity methods, such as Basis Pursuit (BP), which has complexity O(n3). Meanwhile, low-complexity greedy algorithms do...
Global motion estimation (GME) plays an important role in many video application systems such as video coding system MPEG-4. However, its computational complexity is very high. Fast algorithms are needed. In this paper, we propose an improvement to the GME algorithm. We achieve this by introduce two techniques. Firstly, integral projection algorithm (IPA) is used to get first translation estimation...
A wavelet based compressed sensing super resolution algorithm is developed, in which the energy function optimization is approximated numerically via the regularized orthogonal matching pursuit. The proposed algorithm works well with a smaller quantity of training image patches and outputs images with satisfactory subjective quality. It is tested on classical images commonly adopted by super resolution...
In asymmetric stereoscopic video coding, one view can be coded in a lower resolution of the other. In this scenario, stereoscopic video can be compressed with only moderately increased bandwidth and complexity compared to 2D monoview video coding. The subjective quality degradation of this scenario can be negligible compared to coding two views with original resolution. The low-resolution view can...
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