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In this paper a parametric point spread function (PSF) estimation method is presented. This method can be employed for estimating the parameters of linear motion blur vector. The proposed method works on single image and estimates angle and length of motion blur vector to generate required PSF for deblurring. This method is based on step-by-step estimation of motion blur vector. In the first approximation the blind method considers short length vectors in all directions and deblurs image with PSF of these candidate vectors. The quality of deblurred image is assessed by a no-reference quality measurement metric which is proposed in this paper. The proposed no-reference image quality metric evaluates degradation in sharpness of edges and the amount of resulted artifact caused by saturated pixels, in the deblurred image. Those motion vectors that caused unacceptable deblurring results are omitted in the next iteration. The approximation is improved by increasing the length of the remaining vectors and the same process continues iteratively. The process goes on until only one vector remains as the estimation for motion blur vector. Experimental results show that in estimation of motion blur vectors, the length estimation error is less than one pixel in 85% of the cases and angle estimation error in 95% of the cases is less than one degree. Comparing with a conventional method indicates that the proposed method shows more than four times improvement in length estimation and 10% improvement in angle estimation. Moreover, the proposed method has comparatively lower computational load than other conventional deblurring methods.

A reliable and effective packet delivery system is challenging issue due to VANET’s intense characteristics. Broadcast nature of opportunistic routing makes it more effective than the traditional routing in packet delivery and improves the efficiency of sending packets. One of the main problems in VANETs is the packet loss due to collision. Because of the rapid movement of vehicles, frequent changes in topology and the occupancy of the channel by beacon packets, high number of collisions occur in VANETs. In this paper, a new collision-aware opportunistic routing protocol called SCAOR is proposed for highways. To reduce collisions, the design of the SCAOR protocol is composed of two phases. In the first phase, a new metric called QAD, is presented and it contains three parameters of link quality, packet advancement, and node density. In the second phase, a cluster-based scheduling algorithm called CSA is provided for sending periodic beacon packets to reduce the occupancy time of the channel. The QAD metric along with the CSA scheduling algorithm alleviate collisions and improve the efficiency of the SCAOR protocol with respect to the specific features of the VANETs. The performance of the SCAOR routing protocol is compared to the EXOR and LSGO opportunistic routing protocols. Experimental results show improved network performance in terms of packet delivery, end-to-end delay, packet loss and throughput.

Broadcast of beacon messages including geographic coordinates, node speeds, and directions are among the most commonly used methods in routing protocols of VANETs to obtain neighboring positions. Broadcast of periodic beacon messages in fixed time intervals will reduce network performance due to increased channel load and contention. In this paper, an adaptive update strategy for sending beacon messages according to the VANETs’ characteristics (position, speed, and direction) and the nature of broadcast wireless channel in an opportunistic routing strategy is studied. It is based on two rules: 1) an estimation of the lifetime of the links between vehicles’ beacon messages are sent after the expiration of the estimated time to inform their local topology and 2) if the forwarding set of consecutively received data packets is changed, a beacon message is sent to maintain the accuracy of the topology. The simulation results show that the proposed strategy significantly reduces the cost of routing and improves network performance in terms of packet-delivery ratios, average end-to-end delay, and routing overhead.

In this paper, a blind deblurring algorithm that is well converged towards the latent sharp image and the blur PSF is proposed. The proposed algorithm employs our recently proposed ringing suppression regularizer in a solution to the blind deblurring problem. Latent sharp image and the blur PSF are estimated jointly via a multi-functional optimization problem addressed by a first-order primal-dual solver. This approach generates a multi-resolution pyramid of the input blurred image and propagates the estimation results of the lower resolution levels to the higher resolution ones. By considering the PSF initiation as an important step in the blind deblurring process, we introduce a PSF initiation method that benefits from the salient structure obtained via an l0-gradient smoothing of blurred image. A first-order primal-dual solution to the l0-gradient smoothing problem is introduced in this paper. Experimental results show that the proposed deblurring algorithm significantly enhances the results favorably against other state-of- the-art image deblurring methods.

Video abstraction allows indexing, searching, browsing and evaluating a video only by accessing its useful contents. Several studies have been done in this field, but most of them are in pixel domain and require decoding process. It makes these methods more time and process consuming than compressed domain video abstraction. In this paper, we present a new video abstraction method in H.265/HEVC compressed domain, HVAIF. The method is based on the normalized histogram of extracted I-frame prediction modes from an H.265/HEVC coded video. The frames’ similarity is calculated by intersecting their I-frame prediction modes’ histogram. The similarity measure detects and removes redundant key-frames to increase the quality of final video abstraction. Moreover, we employ fuzzy c-means clustering to categorize similar frames and extract key-frames as representatives of the entire video frames. The interpretation of the results shows that using the proposed method achieves on average 86% accuracy and 19% error rate in compressed domain video abstraction which is higher than the other tested methods in the pixel domain. Also, it has an acceptable robustness to coding parameters, and on average it generates video key-frames that are closer to human summaries.

We address the problem of resource allocation for bag-of-tasks (BoT) workflows in a federation of clouds and formulate it as an integer linear programming problem. The proposed model minimizes financial cost including fees for running VMs and fees for data transfer, and fulfills deadline and resource constraints in the clouds. We also formulate the problem of BoT scheduling in the hybrid clouds, and compare the financial cost in the federation of clouds with that in the hybrid clouds. Moreover, this paper discusses sensitivity analysis to investigate stability in the related allocation problem. Numerical results show that the resource allocation in the federation is considerably preferred to that in the hybrid clouds in terms of stability and cost-saving. In this paper, we also propose an approach named GRASP-FC for obtaining an approximate optimal solution of BoT scheduling in the federation. GRASP-FC is an extension of greedy randomized adaptive search procedure (GRASP), and it can be of great interest from the computational points of view.

Video compression performance of High Efficiency Video Coding (HEVC) is about twice of H.264/AVC video compression standard. The improvement in coding efficiency in HEVC is achieved by considerable increase in the computational load compared to H.264/AVC which is substantially very computational intensive. One of the units in HEVC which has changed considerably compared to H.264/AVC is Integer Discrete Cosine Transform (IDCT) unit. IDCT in HEVC standard includes 32 × 32, 16 × 16, 8 × 8 and 4 × 4 transforms. In this paper, a hardware solution for implementing the entire inverse IDCTs in HEVC decoder is proposed. The proposed hardware has a resource-sharing pipelined architecture. As a result, the hardware resources and computation time for implementing inverse IDCTs in HEVC decoder are reduced. Synthesis results by using NanGate OpenPDK 45 nm library indicate that the proposed hardware can achieve 222 MHz clock rate and can achieve real-time decoding of 4096 × 3072 video sequences with 70 fps.

In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multi-resolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art image deblurring methods.

Motion vectors (MVs) are the most common temporal descriptors in video analysis, indexing and retrieval applications. However, video indexing and analysis based on MVs do not perform well for videos at different dimension ratios (DRs) or even various resolutions. As a result, video indexing and analysis which are based on identifying similar video face with many difficulties at different DRs or resolutions by MVs. In this study, a two-stage algorithm is introduced to make MV descriptors robust against variations first in DR and then at resolution. In the experiments performed on motion vector histograms, the proposed method improves the performance on identifying similar videos at various spatial specifications by up to 73%. Moreover, in the video retrieval experiments, the proposed modified MV outperforms original MV feature vector. This is an indication of improvement in differentiation of similar and dissimilar videos by the proposed temporal feature vector.

Instability of wireless networks and high mobility of vehicles make effective and reliable delivery of packets in VANETs a challenging issue. Anypath routing is proposed to tackle this issue in multi hop VANET communications. Link stability and quality parameters are used in cost metrics proposed for Anypath routing. Both of them prefer near hops in the forwarding set and as a result, the number of hops and delay in Anypath VANET communications are increased. In this paper a new cost metric for Anypath VANET routing is proposed to reduce the hop count and delay and increase the network throughput and the packet delivery ratio. The simulation results indicate superior performance of the proposed cost function over other tested methods such as SAF and LLA.

A mathematical programming model is proposed for a resource allocation problem in federated clouds, where bag-of-tasks (BoT) applications are assigned to instance types with different costs and performance levels. The proposed model is a binary linear programming problem containing deadline and resource constraints in the cloud federations and by the objective of minimizing the total cost of applications. These constraints and objective are explicitly expressed using mathematical functions, and the model is solved with the CPLEX solver. This paper also discusses a post-optimality analysis that deals with stability in assignment problems. Numerical results show that the optimal cost and optimal solutions in the cloud federations are lower and more stable, respectively, than those presented by single-provider clouds. In contrast to optimality in single-provider clouds, that in the cloud federations is less sensitive to input data. The cost of bag-of-tasks applications in a federated cloud is minimized.Cost minimization is modeled as a binary linear programming problem.The proposed model is solved with the CPLEX solver.The sensitivity of optimal solutions to input data is investigated.

Dynamic topology and unstable wireless links make efficient data delivery in vehicular ad-hoc networks (VANETs) a challenging issue. To tackle this issue, several routing strategies for finding a route with high reliability and low latency in VANETs have been proposed. One of theme is Opportunistic routing (OR) paradigm which can improve the reliability of routing in vehicular ad-hoc networks (VANETs) by broadcasting transmission features and employing additional backup links from other neighboring nodes. In this paradigm a node sends the packet to a number of its neighbors called forwarding set. The way transmission priority is assigned to the nodes in the forwarding set can highly affect the performance of opportunistic routing. In this Paper a new measure composed of three parameters is proposed for selecting the best forwarding node. Simulations on highway environment indicate that the proposed method improves the routing performance in all grounds of QoS compared to the other published methods in the literature.

Fast content access and content based retrieval of images are among the common web and signal processing applications; on the other hand, JPEG is the dominant format for image compression in a wide range of applications. In this paper, a simple and fast method for content access and retrieval of JPEG coded images is presented which does not require complete decompression of coded images. The presented method uses DCT coefficients of coded blocks in the JPEG bit stream to extract average values of various size picture blocks namely DC values. The DC values provide an approximation of the coded image, which can be employed to construct a lower resolution picture or color histogram of the JPEG coded image for retrieval and other applications without full decompression of the image. A novel fast algorithm to access the content of JPEG compressed images is presented.Extracting 2×2 and 4×4 sub-block averages from JPEG bitstream without full decompression.The extracted sub-block averages can directly construct color histogram or an approximated picture.The presented method can be efficiently used for fast exploring or retrieval of JPEG coded images.Finding block averages from JPEG bitstream at the same accuracy as earlier works but faster.

High efficiency video coding (HEVC), the latest international video coding standard, greatly outperforms previous standards such as H.264/AVC in terms of coding bitrate and video quality. The coding efficiency improvement in HEVC is achieved by introducing several new techniques such as recursive quad-tree structure and increased number of intra prediction modes. However, computational load is also increased due to employing the new techniques. In this paper, we propose a solution for fast I-frame coding in HEVC standard using homogeneity of Coding Units (CUs). The proposed solution consists of two stages. In the first stage, we evaluate CU homogeneity by computing a parameter named dominant direction strength and predict CU size by this means. In the second stage, we select 11 modes out of 35 for the specified CU size based on dominant direction of the CU. Experimental results indicate that the proposed method achieves on average 45.8 % reduction on coding time, with very similar coding efficiency as the HEVC reference software. Moreover, we designed tree-stage pipelined architecture for our method which can operate at 235 MHz maximum clock rate which means it can be used for real-time coding of all intra configuration of HEVC videos up to level 6.2.

In the high-efficiency video coding (HEVC) standard, intra prediction has higher computational complexity compared with H.264/AVC (advanced video coding) because of increasing the number of intra prediction modes and also higher number of coding unit (CU) sizes. The HEVC encoder evaluates 35 prediction modes on five possible prediction unit (PU) sizes to find the one with the minimum rate–distortion cost. Although this approach improves coding efficiency, it is very time-consuming. In this paper, we propose a fast intra prediction method to reduce the complexity of I-frame coding. The proposed method consists of three stages which is based on smoothness spatial feature. In the first stage, a measure is introduced to estimate CU smoothness by using sum of absolute differences (SAD) among CU pixels in four directions. By considering that a smooth region can be predicted with larger CUs, when the measured smoothness parameter is lower than a predefined threshold, only the prediction modes in the current CU are evaluated. In the second stage, the number of intra prediction modes is reduced based on the calculated SADs in the previous stage. In the last stage, if the first three candidate modes resulted from rough mode decision stage in the previous PU and the current PU are similar, then the best mode prediction of the previous PU is selected as the best candidate mode. Experimental results indicate that the proposed method can reduce the coding time on average to 43 % and maintain coding video quality, whereas bitrate increases negligibly (0.5 %).

Compressed domain retrieval is important in the retrieval of HEVC coded videos, because full decompression of HEVC coded videos is very time consuming. In this paper, a texture based retrieval method is proposed for either video retrieval of HEVC coded videos or image retrieval of coded images as HEVC I-frames. The various prediction unit sizes and high number of prediction modes which are employed in I-frame coding of HEVC, provides higher information about the texture of the coded picture compared to the I-frame coding in H.264/AVC. The proposed texture based image retrieval method for HEVC coded visual information is based on histograms of prediction modes and prediction unit sizes. This method achieves 0.34 ANMRR in the image retrieval experiments and 0.33 ANMRR in the conducting video retrieval experiments, which are better compared to the other compressed domain retrieval methods based on H.264/AVC. On the other hand, the experimental evaluations indicate good robustness of the proposed method against variations in quantization parameter and image intensity. Moreover, the timing analysis indicates that feature extraction time by the proposed method is about 35 % of the decoding time of a coded video.

High efficiency video coding (HEVC) standard, introduced by the joint collaborative team on video coding (JCT-VC), is the newest international standard for video compression. This standard provides more compression and better video quality, compared with the previous standards such as H.264/AVC. The higher compression efficiency in HEVC is achieved at the cost of increasing the computational load. Intra prediction unit is among the high computational stages in the HEVC encoder. There are proposed 35 intra prediction modes in HEVC, to improve the compression efficiency. To reduce the computational load of intra prediction, HEVC uses a preprocessing step, called Rough Mode Decision (RMD). A number of best prediction modes are selected by the RMD stage and then, using Rate Distortion Optimization (RDO) process, the encoder selects the best prediction mode among them. In this paper a two stage algorithm is proposed for fast intra mode decision in HEVC encoder, in the first stage of our proposed algorithm the number of the tested modes in RMD is reduced from 35 to 19. In the second stage, the number of the tested modes in the RDO process is reduced as well. In order to evaluate the performance of the proposed method two encoding profiles, main and main10 were used in the experiments. Experimental results indicate that the proposed method achieves 14.4 and 7.2 % reduction in the encoding time compared with the HEVC reference software in all intra-main and all intra-main10 configurations, respectively. Meanwhile, it imposes minimum reduction in the coding efficiency among the compared methods.

A new feature vector based on motion structure of video sequences is proposed which can be used independently or along with motion vectors in video analysis and retrieval applications. The proposed feature vector employs size of prediction units (PUs) in P and B frames of high efficiency video coding (HEVC) coded videos. The feature vector can be extracted by partial decoding of compressed video in the same decoding stage as motion vectors. Video retrieval application is selected to evaluate the performance of the new feature vector. In experimental evaluations, the proposed temporal feature vector independently shows on average 12% improvement in performance against motion vectors and by using it along with motion vectors, the performance of motion vector improves on average by 32% in average normalised modified retrieval rank (ANMRR) measure.

In this paper we propose a blind and highly robust watermarking method consisting of two embedding stages. In the first stage, the odd description of image is divided into non-overlapped fixed size blocks and the signature (watermark) is embedded in the high frequency component of the Contourlet transform (CT) of the blocks. In the second stage, the signature is embedded in the low frequency component of the global CT of the image. The main issue associated with two-stage blind watermarking is the selection of the less affected signature among the two embedded signatures. In this paper a measure is introduced to decide between the two extracted signatures. Simulation results indicate that the proposed method achieves higher robustness compared to other known watermarking methods. Moreover, since watermark is embedded in the local as well as global CT coefficients of two different frequency bands, the proposed method is robust against a wide range of attacks. This is due to the fact that most of the attacks affect either a specific frequency band or a specific location in the watermarked image.

Video indexing is employed to represent the features of video sequences. Motion vectors derived from compressed video are preferred for video indexing because they can be accessed by partial decoding; thus, they are used extensively in various video analysis and indexing applications. In this study, we introduce an efficient compressed domain video indexing method and implement it on the H.264/AVC coded videos. The video retrieval experimental evaluations indicate that the video retrieval based on the proposed indexing method outperforms motion vector based video retrieval in 74 % of queries with little increase in computation time. Furthermore, we compared our method with a pixel level video indexing method which employs both temporal and spatial features. Experimental evaluation results indicate that our method outperforms the pixel level method both in performance and speed. Hence considering the speed and precision characteristics of indexing methods, the proposed method is an efficient indexing method which can be used in various video indexing and retrieval applications.

Motion vectors histogram (MVH) is an effective feature vector in video indexing and analysis applications. One of the challenges in the use of motion vector histogram is that it is not robust to video aspect ratio (AR). As a result, it is not suitable for comparing videos with different ARs. In this paper a feature vector based on motion vectors in H.264/AVC is introduced which is robust to variations in aspect ratio. This feature vector, namely scaled motion vector histogram (SMVH), has superior performance compared to common MVH and experimental results indicates that SMVH achieves on average 10% improvement in similarity detection of videos with different ARs compared with MVH.

One of the computationally intensive stages in the H.264/AVC encoder is intra prediction. This is because rate-distortion optimization (RDO) is performed for the entire possible modes to select the optimum intra prediction mode. Fast intra mode decision methods have been introduced to reduce the number of tested modes by RDO to a few prediction modes. In this paper, a new fast mode decision method is introduced for H.264/AVC encoder. The proposed method is based on the idea that when one of the prediction modes achieves good RDO, its orthogonal prediction mode will not perform well. As a result, we select by simple measures only one of the orthogonal modes and perform RDO only for the selected modes. Simulation results indicate that the proposed method achieves higher peak signal-to-noise ratio (PSNR) and lower bit-rate when compared with other proposed fast mode decision methods with hardware realization. Moreover, we have proposed a three-stage pipelined architecture for our fast mode decision method, which can operate at 175 MHz maximum clock rate. Synthesis results indicate that the three-stage pipelined architecture achieves lower gate count and higher maximum clock rate when compared with hardware realizations for other fast mode decision methods and can be used to encode real-time videos of H.264/AVC standard up to level 5.1.

Automatic analysis of human motion includes initialisation, tracking, pose recovery and activity recognition. In this paper, a computing framework is developed to automatically analyse human motions through uncalibrated monocular video sequences. A model-based kinematics approach is proposed for human gait tracking. Based on the tracking results, 3D human poses and gait features are recovered and extracted. The recognition performance is evaluated by using different classifiers. The proposed method is advantageous in its capability of recognising human subjects walking non-parallel to the image plane.

Psoriasis is an autoimmune skin disease, afflicting skin with red plaques that are usually accompanied by silvery-white scales. Various medical treatments are used, with different impacts on the patients, but there is no definite cure for the disease. The PASI standard is employed to measure the performance of the treatments. It includes four parameters, namely area, erythema, scaliness and skin thickness. The PASI parameters are usually measured manually by physicians through subjective clinical observations which are imprecise, time consuming and in some cases lead to diverse results. This paper presents a computer-based automatic method to measure the area parameter in the PASI standard. In the proposed method, the YCbCr colour space is used to differentiate the plaques from the skin by applying an optimal threshold method. Performance evaluation results indicate that the proposed method is able to determine lesion areas with accuracy higher than 96% for 18 out of 20 cases and higher than 92% for another case. As well as high accuracy the proposed method has another advantage over previous methods: it can automatically detect plaques with silvery-white scales, plaques on hairy skins and tiny plaques, as well as simple (scale-less) plaques.

A fast and simple method for content based retrieval using the DC-pictures of H.264 coded video without full decompression is presented. Compressed domain retrieval is very desirable for content analysis and retrieval of compressed image and video. Even though, DC-pictures are among the most widely used compressed domain indexing and retrieval methods in pre H.264 coded videos, they are not generally used in the H.264 coded video. This is due to two main facts, first, the I-frame in the H.264 standard are spatially predicatively coded and second, the H.264 standard employs Integer Discrete Cosine Transform. In this paper we have applied color histogram indexing method on the DC-pictures derived from H.264 coded I-frames. Since the method is based on independent I-frame coded pictures, it can be used either for video analysis of H.264 coded videos, or image retrieval of the I-frame based coded images such as advanced image coding. The retrieval performance of the proposed algorithm is compared with that the fully decoded images. Simulation results indicate that the performance of the proposed method is very close to the fully decompressed image systems. Moreover the proposed method has much lower computational load.

A fast and simple method for content accessing and extracting the DC-pictures of H.264 coded video without full decompression is presented. Since DC-pictures are used in various applications such as compressed domain analysis or indexing of visual data, several methods for extracting DC-pictures of non-integer DCT codecs have been proposed. However, these methods are not efficient for the H.264 codec that uses integer DCT transform, which employs only shift and add operations and already has lower computational load than the non-integer DCT transform. In this paper we propose a fast and low complexity method to extract DC-pictures of H.264 coded videos with significant lower computational cost compared to its full decompression.

In this paper we introduce a new compressed domain texture based visual information retrieval method. The proposed method is for the spatially predicted I-frames in the H.264 video coding standard. In I-Frame coding various prediction modes can be used for spatially predicting pixels of a block from upper or left side adjacent pixels. The selected prediction mode for a block indicates the way in which pixels of that block are related to their neighboring parts. Hence, we propose to use the histogram of the prediction modes as a texture descriptor of compressed I-frames. Since the method is based on independent I-Frame coded pictures, it can be used either for video analysis of H.264 coded videos or image retrieval of the I-frame based coded images such as advanced image coding. The simulation results indicate that this method has superior performance and lower computational load compared to an efficient realization of pixel domain texture retrieval method based on Gabor filter. Moreover, it is also robust to variations in image and coding parameters. Hence, this method is a powerful tool for visual information analysis in a wide range of applications.

In this paper we propose a fast and efficient image retrieval method for searching JPEG2000 compressed image databases. Comparing image contents of the JPEG2000 coded images in the pixel domain requires image decompression, which imposes intensive computational processes of the inverse discrete wavelet transform and the arithmetic decoding. On the other hand, the first decoding stage of the JPEG2000 standard is the packet header decoding, which is a simple process, but it provides valuable information about the code blocks in the packet. In this paper we exploit packet header information including the number of non-zero bit-planes, the number of coding passes and the code block length for comparing the JPEG2000 compressed images. Experimental results show that the proposed method provides a better performance compared to other JPEG2000 compressed domain retrieval techniques and even outperforms the pixel-based image retrieval techniques such as the Gabor filter; moreover the proposed method is very fast with very low computational load.

The Segmentation symbol is an error resilience feature in the JPEG2000 image coding standard. When this feature is used, lack of detection of the segmentation symbol at the end of a bit-plane by the decoder indicates that an error has occurred within that bit-plane. However, a detected and correctly decoded segmentation symbol does not guarantee that the bit-plane is error free. In this paper, an error detection method is proposed for detecting and removing the artifacts caused by the damaged bit-planes that were not detected by the segmentation symbol. The proposed method provides a model for the coded bit-planes in the encoder and uses this model for error detection at the decoder. Extensive simulations verify that the proposed method can effectively remove artifacts and improve the quality of the corrupted images on average by 1.2 dB and in some pictures by up to 6 dB in PSNR1.

Intra coding in the High Efficiency Video Coding (HEVC) standard can significantly improve the compression efficiency but increasing the number of intra prediction modes and also higher number of Coding Unit (CU) sizes in the HEVC standard imposes much higher computational load compared with intra prediction in H.264/AVC, which substantially is computational intensive. To reduce the intra coding complexity in HEVC, this paper presents an early termination algorithm for intra prediction. The proposed method is based on the fact that a homogenous region can be predicted with larger CUs. As a result, when the proposed smoothness parameter is lower than a predefined threshold, only the prediction modes in the current CU are evaluated. Experimental results indicate that the proposed algorithm can provide on average 27.5% time saving with only 0.4% BD-rate loss whereas it maintains the same coding video quality compared with HEVC reference software, HM15.0, in all intra-main configuration.

In this paper, a texture based retrieval method is proposed in HEVC compressed domain. The proposed method can be used for either video retrieval of HEVC coded videos or image retrieval of HEVC I-frame coded images. In I-frame coding various prediction modes are utilized to spatially predict pixels of a block. The selected prediction mode and the size of prediction unit for coding of a block indicate the way, in which the pixels of the block are related to their neighboring pixels and they can considered as texture features. Thus, we measured the texture similarity of coded I-frames in HEVC based on the similarity of the prediction modes and block size histograms. This retrieval method achieves 0.34 ANMRR in videos with high resolution, which is better compared to the other compressed domain retrieval method based on previous standard H.264/AVC that have achieved 0.45 ANMRR in the same image database.

In order to achieve higher compression performance the amendment called Fidelity Range Extension (FRExt) was added to the H.264 Advanced Video Coding (AVC) standard which uses both 4×4 and 8×8 integer discrete cosine transform (DCT) adaptively in the high profiles. This led to additional complexity of the initial version of the H.264/AVC encoder which had substantially high computational complexity. In this paper we propose a new algorithm which reduces the computational complexity for software implementation of horizontal 8×8 Integer DCT by more than 25 percent. Simulation results indicate 22 percent reduction in the computation time by using the proposed algorithm.

In this paper, a new method for extracting Farsi text from a video in the wavelet domain is proposed. In an image including text, due to the complexity of the background and the quick changes of contrast in the text areas, the energy intensity variation is high. Therefore, by exploiting this property a method is proposed for extracting text. Moreover, for increasing the accuracy of the proposed method, some motion characteristics of the movies have been used. The proposed method is able to localize both Farsi and Latin Text in video with a high performance. The experimental results confirms the effectiveness of the proposed method.

One of the most important aspects of image quality is contrast and this paper presents a new hybrid method for contrast enhancement. The proposed method is a combination of two basic contrast enhancement methods i.e. transform and histogram. At first we apply Nonsubsampled Contourlet Transform (NSCT) on the source image, then NSCT coefficients are mapped to fuzzy domain and modified by a mapping function in fuzzy domain. After transforming the modified membership values from fuzzy domain into frequency domain, the enhanced image is reconstructed from the modified NSCT coefficients by inverse NSCT. Finally, histogram of the image is equalized by Contrast Limited Adaptive Histogram Equalization (CLAHE). The experimental results show that our method can achieve higher improvement than both Contourlet Transform (CT) and Histogram Equalization (HE) contrast enhancement methods.

Binary Arithmetic Coding (BAC) is among the techniques used in H.264 video coding standard to improve the coding efficiency. BAC includes an iterative process of renormalization with up to seven iterations for coding each symbol. Since BAC is also a computational intensive unit in H.264 encoder, various hardware realizations have been proposed for it in the literature. In this paper, we propose a hardware implementation for BAC, which uses lookup table to avoid the iterative coding process and achieves coding rate of one symbol per clock at 260 MHz clock rate. Post synthesize simulation results indicate that the proposed architecture is a resource and speed efficient hardware for H.264 binary arithmetic encoder.

Content based image retrieval is a challenging issue in management of existing large digital image libraries and databases. The accuracy of image retrieval methods is subject to effective extraction of image features such as color, texture, and shape. In this paper we propose a new image retrieval method using contourlet transform coefficients. We use the properties of contourlet coefficients to assign the normal distribution function to the distribution of coefficients in each sub-band. The assigned normal distribution functions are used to extract the texture feature vector at the next stage. Simulation results indicate that the proposed method outperforms other conventional texture image retrieval methods such as, Gabor filter and wavelet transform. Moreover, this method shows a noticeable higher performance compared to another contourlet based method.

The motion JPEG2000 (MJP2) video coding standard uses intraframe compression and provides practical features such as fast coding, easy editing, and error robustness. Error robustness and concealment are some of the crucial requirements of any decoder in error prone applications. In this paper, a new error concealment method for MJP2 decoder has been proposed. The proposed method exploits the remaining temporal redundancies within the MJP2 coded video sequences for error concealment. The realized MJP2 decoder is able to detect and remove the artifacts produced by highly damaged bit planes that were not detected by the segmentation symbol, one of the error resilience tools in MJP2. Simulation results indicate that the proposed decoder can effectively remove such artifacts in the decoded erroneous MJP2 bit streams and improve the PSNR of the corrupted frames by up to 6.8 dB. An average of 0.2 dB improvement has been observed over a relatively large number of test cases and various error rates.

In this paper we propose a new indexing method for comparing image contents in the JPEG2000 compressed domain. Comparing image contents of the JPEG2000 coded images in the pixel domain requires image decompression, which imposes intensive computational processes of the inverse discrete wavelet transform and the arithmetic decoding. On the other hand, the first decoding stage of the JPEG2000 standard is packet header decoding, which is a simple process, but provides valuable information about the code blocks in the packet. In this paper we use packet header information for JPEG2000 image indexing. The proposed method exploits full packet header information including the number of non-zero bit-planes, the number of coding passes and the code block length for indexing the JPEG2000 compressed images. Experimental results show that the proposed method provides a better performance compared to other JPEG2000 compressed domain indexing techniques and even outperforms the pixel-based image indexing techniques such as the Gabor filter.

In this paper we introduce a compressed domain texture retrieval method for I-frame coded pictures in the H.264 standard and study its robustness against the image and coding parameter changes. The proposed image retrieval method uses the intra mode prediction histogram of the I-frame coded pictures as the feature vector. The simulation results show that this is a very efficient compressed domain image retrieval method and is also robust to various image and coding parameter changes. The robustness of the proposed feature vector indicates that the proposed method can be used in a wide range of compressed domain image and video retrieval applications.

In order to achieve higher compression performance the fidelity range extension (FRExt) amendment was added to the H.264 advanced video coding (AVC) standard. It uses both 4 × 4 and 8 × 8 integer discrete cosine transform (DCT) adaptively in the high profiles. This led to additional complexity of the initial version of the H.264/AVC encoder which had substantially high computational complexity. In this paper, we propose a new algorithm which reduces the computational complexity for software implementation of horizontal 8 × 8 integer DCT by more than 25%. Simulation results indicate 22% reduction in the computation time by using the proposed algorithm.

A novel error concealment method is introduced for lost LL sub-band in a Motion JPEG 2000 image sequence. This method employs high frequency sub-bands (LH, HL and HH) in the current frame and the correctly received previous frame for recovery of lost LL sub-band in the current frame.

In this paper a new texture descriptor is introduced for visual data. The proposed descriptor is based on the information of I-frame coding in H.264 video coding standard. In I-frame coding different prediction modes can be used for predicting a block from upper or left side neighboring pixels. The selected prediction mode for a block in an I-frame indicates the way that the block depends on the neighboring parts. In this paper, we use the prediction mode histogram of a compressed I-frame picture, as a texture descriptor for image or video retrieval. Simulation results comparing the proposed method with Gabor filter show that the proposed method is an efficient texture descriptor in compressed domain.