Block Matching Video Compression Based on Sparse Representation and Dictionary Learning

Both interclass variances and intraclass similarities are crucial for improving the classification performance of discriminative dictionary learning (DDL) algorithms. However, existing DDL methods often ignore the combination between the interclass and intraclass properties of dictionary atoms and coding coefficients. To address this problem, in this paper, we propose a discriminative Fisher embedding dictionary learning (DFEDL) algorithm that simultaneously establishes Fisher embedding models on learned atoms and coefficients. Specifically, we first construct a discriminative Fisher atom embedding model by exploring the Fisher criterion of the atoms, which encourages the atoms of the same class to reconstruct the corresponding training samples as much as possible. At the same time, a discriminative Fisher coefficient embedding model is formulated by imposing the Fisher criterion on the profiles (row vectors of the coding coefficient matrix) and coding coefficients, which forces the coding coefficient matrix to become a block-diagonal matrix. Since the profiles can indicate which training samples are represented by the corresponding atoms, the proposed two discriminative Fisher embedding models can alternatively and interactively promote the discriminative capabilities of the learned dictionary and coding coefficients. The extensive experimental results demonstrate that the proposed DFEDL algorithm achieves superior performance in comparison with some state-of-the-art dictionary learning algorithms on both hand-crafted and deep learning-based features.

In this paper we show that the computational complexity of the iterative thresholding and K-residual-means (ITKrM) algorithm for dictionary learning can be significantly reduced by using dimensionality-reduction techniques based on the Johnson–Lindenstrauss lemma. The dimensionality reduction is efficiently carried out with the fast Fourier transform. We introduce the iterative compressed-thresholding and K-means (IcTKM) algorithm for fast dictionary learning and study its convergence properties. We show that IcTKM can locally recover an incoherent, overcomplete generating dictionary of $$K$$ atoms from training signals of sparsity level $$S$$ with high probability. Fast dictionary learning is achieved by embedding the training data and the dictionary into $$m< d$$ dimensions, and recovery is shown to be locally stable with an embedding dimension which scales as low as $$m = O(S\log ^4S\log ^3 K)$$. The compression effectively shatters the data dimension bottleneck in the computational cost of ITKrM, reducing it by a factor O(m/d). Our theoretical results are complemented with numerical simulations which demonstrate that IcTKM is a powerful, low-cost algorithm for learning dictionaries from high-dimensional data sets.

H.264/AVC, the latest video coding standard, adopts rate-distortion optimization (RDO) technique to obtain the best intra- and inter-prediction, while maximizing visual quality and minimizing the required bitrate. However, the full RD cost calculation for all intra-prediction modes, the exhaustive searches for finding optimal motion vectors for all block sizes, and the multiple references frame procedure considerably increase its computational complexity with the allowed number of prediction modes. The authors have previously proposed an approach for both inter- and intra-mode decisions that takes into account the several effective parameters, image content type, quantization parameter, correlation of motion vectors and motion details of video objects. In order to reduce the complexity, here we propose a new two-step adaptive search range decision by developing a combination of previous algorithm. The first step of our adaptive search range decision determines the search range for current MB according to the motion vectors of the previous MBs. In the second step, the motion vector obtained in the current frame is used to adaptively searching the window size of the previous reference frame for multi-frame motion estimation. As such, only a subset of inter- and intra-modes is chosen for RDO calculation. Experimental results show that the proposed algorithm reduces the total encoding cost with negligible loss in PSNR and a slightly increase in the required bitrate, when compared to our previous algorithm and other algorithms reported in the literature.

This study aimed to explore the effect of ultrasound imaging in the diagnosis and evaluation of acupoint application in the treatment of idiopathic edema. In this study, an ultrasound imaging diagnosis based on the segmentation dictionary learning (S-DL) algorithm was proposed. In addition, the autoencoding algorithm (ASE) was compared with the traditional dictionary learning (DL) algorithm. The treatment effect, associated quantitative integral, and quality of life score of patients in two groups were compared. The results showed that the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity (SSIM) of the S-DL algorithm were 32.45 dB, 0.654, and 0.0012, respectively, which were quite different compared to the ASE and DL algorithms, showing statistical significance (P < 0.05). As the noise level increased, the image reconstruction quality gradually decreased, but the S-DL algorithm obtained better image quality than the DL and ASE algorithms, and the difference was statistically great (P < 0.05). There was no significant difference in the average age and average course of the disease between the experimental group and the control group (P > 0.05). The overall treatment effect of patients in the experimental group was 96.77%, while that in the control group was 45.16%, and the difference between the two was statistically significant (P < 0.05). After treatment, the semiquantitative scores of fatigue, dizziness, palpitation, frequent urination, urgent urination, and dyspepsia of the experimental group were 1.18, 0.39, 0.72, 1.21, and 0.87, respectively, which were much lower than those of the control group statistically (P < 0.05). The score of quality of life of the experimental group of patients after treatment was 91.27 points, and that of the control group was 82.35 points, showing statistically great difference (P < 0.05). It showed that the algorithm performance of S-DL was relatively good, and the acupoint application therapy was better than traditional western medicine in the treatment of idiopathic edema, which reduces the discomfort of patients to a certain extent and improves the quality of life of patients.

Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

The goal of dictionary learning algorithms is to learn a set of atoms called dictionary from a set of training data such that each training data can be represented sparsely by the dictionary. Most dictionary learning algorithms use two alternating steps, sparse coding and dictionary update, to solve this problem. In this paper, we propose a new online dictionary learning algorithm with a novel dictionary update step. In the new update method, only the atoms involved in the sparse representation of new training data are adaptively updated. The adaptive update also includes using the previous training data that have common atoms with the new training data in their sparse representation. The proposed algorithm reduces the computational complexity by reducing the number of atoms updating at each iteration and the number of training data contributing in the dictionary update. In the proposed algorithm, different weights are given to training data, so each has the proper level of influence in the dictionary update. The simulation results show this improves the performance of the algorithm, in convergence speed and representation error. We also show that our algorithm has the ability of full recovery of dictionary with only one trial over training data and does not need any dictionary pruning to remove the unused atoms of the dictionary. We compare our proposed algorithm with well-known online and batch based methods using synthetic and autoregressive data.

Retinal blood vessels are considered as being interference on the retinal images for the task of detecting significant features of the most frequent eye diseases. If these blood vessel structures could be suppressed, it might lead to a more accurate segmentation of retinal lesions as well as a better extraction of textural features to be used for pathology detection. This work proposes, as a novelty, the use of sparse representations and dictionary learning techniques for retinal vessel inpainting. The dictionary learning algorithms used in this paper were the Recursive Least Square Dictionary Learning (RLS-DL), and Online Dictionary Learning (ODL). We tested the performance of the algorithm for grayscale and RGB images from the DRIVE public database, employing different neighbourhoods and sparseness factors. An average recovery error smaller than 0.022 was achieved. The results suggest that the use of sparse representations and dictionaries learned by RLS-DLA performs very well for inpainting of retinal blood vessels.

Dictionary learning is still a challenging problem in signal and image processing. In this paper, we propose an efficient block-structured incoherent dictionary learning algorithm for sparse representations of image signals. The constrained minimization of dictionary learning is achieved by iteratively alternating between sparse coding and dictionary update. Without relying on any prior knowledge of the group structure for the input data, we develop a two-stage clustering method that identifies the underlying block structure of the dictionary under certain restricted constraints. The two-stage clustering method mainly consists of affinity propagation and agglomerative hierarchical clustering. To meet the conditions of both the upper bound and the lower bound of the mutual coherence of dictionary atoms, we introduce a regularization term for the objective function to adjust the block coherence of the overcomplete dictionary. The experiments on synthetic data and real images demonstrate that the proposed dictionary learning algorithm has lower representation error, higher visual quality and better reconstructed results than most of the state-of-the-art methods.

Notice of Violation of IEEE Publication Principles<br><br> "Motion vector estimation of video image by Pyramidal implementation of Lucas Kanade Optical flow"<br> by Sukeshini N.Tamgade and Vibha R. Bora<br> in the Proceedings of the 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET), January 2009, pp.914-917<br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br> This paper duplicates content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> "Pyramidal Implementation of the Affine Lucas Kanade Feature Tracker Description of the Algorithm"<br> by Jean-Yves Bouguet<br> in Intel Corporation-Microprocessor Research Labs Report, January 2001<br><br> <br/> Motion vector estimation is an important parameter for video segmentation. Effective video compression can be achieved by choosing a correct approach for the calculation of motion vector. Here in this paper we propose an optical flow motion vector estimation through iterative Lucas-Kanade Pyramidal implementation for both large & small motion In image pyramid representation a group of pixel information is gradually reduced to a value of one pixel information for both current & reference frame. The velocity & displacement at each pixel is obtained by using Lucas-Kanade equations. The original image is recovered by warping reference frame towards current frame using flow vectors i.e. velocity & displacement by using image warping techniques. The process is repeated until convergence. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences.

Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This motivates the design of dictionary learning algorithms that consider distributed nature of data as one of the problem variables. Just like centralized settings, distributed dictionary learning problem can be posed in more than one way depending on the problem setup. Most notable distinguishing features are the online versus batch nature of data and the representative versus discriminative nature of the dictionaries. In this paper, several distributed dictionary learning algorithms that are designed to tackle different problem setups are reviewed. One of these algorithms is cloud K-SVD, which solves the dictionary learning problem for batch data in distributed settings. One distinguishing feature of cloud K-SVD is that it has been shown to converge to its centralized counterpart, namely, the K-SVD solution. On the other hand, no such guarantees are provided for other distributed dictionary learning algorithms. Convergence of cloud K-SVD to the centralized K-SVD solution means problems that are solvable by K-SVD in centralized settings can now be solved in distributed settings with similar performance. Finally, cloud K-SVD is used as an example to show the advantages that are attainable by deploying distributed dictionary algorithms for real world distributed datasets.

Dictionary learning aims to find a dictionary that can sparsely represent the training data. Methods in the literature typically solve the problem by alternating between two stages: sparse coding and dictionary update. In this paper, we propose a novel dictionary learning algorithm using rank-one projection (ROP). The key contribution is that we cast dictionary learning as an optimization with respect to a single variable which is a set of rank one matrices. The resulting algorithm is hence single-stage. An alternating direction method of multipliers (ADMM) is derived to solve the optimization problem and a lower bound of penalty parameter is computed to guarantee a global convergence despite non-convexity of the optimization formulation. From practical point of view, ROP reduces the number of tuning parameters required in benchmark algorithms. Numerical tests demonstrate that ROP outperforms benchmark algorithms for both synthetic and real data.

Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representation problems. However, most existing algorithms assume isotropic noise. This is a restrictive assumption as the noise across samples may be nonuniform in a number of real world application. The aim of this article is to propose a sequential dictionary learning algorithm for measurement data vectors affected by nonuniform noise. As most of the existing algorithms, the proposed method is a two stages iterative algorithm where the effect of the noise nature appears in the sparse coding and dictionary update stages. The cost function used to derive the dictionary update stage is developed based on the link between singular value decomposition and rank one matrix approximation. Results on the simulated dictionary recovery problem and an application on face recognition dataset shows that the proposed algorithm outperforms current state of the art dictionary learning methods.

In the state-of-art video coding standard HEVC, temporal motion vector (MV) predictor is adopted in order to improve coding efficiency. However, motion vector information in reference frames, which is used by temporal MV predictor, takes significant amount of bits in memory storage. Therefore motion data needs to be compressed before storing into buffer. In this paper we propose an adaptive motion data storage reduction method. First, it divides the current 16x16 block in the reference frame into four partitions. One MV is sampled from each partition and all sampled MVs form a MV candidate set. Then it judges if one or two MVs should be stored into the MV buffer by checking the maximum distance between any two of the MVs in the candidate set. If the maximum distance is greater than a certain threshold, the motion data of the two MVs that have maximum distance are put into memory; otherwise the motion data of the upper left block is stored. The basic goal of the proposed method is to improve the accuracy of temporal MV predictor at the same time reducing motion data memory size. Simulation results show that compared to the original HEVC MV memory compression method in the 4th JCT-VC meeting, the proposed scheme achieves a coding gain of 0.5%~0.6%; and the memory size is reduced by more than 87.5% comparing to without using motion data compression.

Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

In recent years, sparse coding has drawn considerable research attention in developing feature representations for visual recognition problems. In this paper, we devise sparse coding algorithms to learn a dictionary of basis functions from Scale- Invariant Feature Transform (SIFT) descriptors extracted from images. The learned dictionary is used to code SIFT-based inputs for the feature representation that is further pooled via spatial pyramid matching kernels and fed into a Support Vector Machine (SVM) for object classification on the large-scale ImageNet dataset. We investigate the advantage of SIFT-based sparse coding approach by combining different dictionary learning and sparse representation algorithms. Our results also include favorable performance on different subsets of the ImageNet database.