nils-herrmann/clean_scirepeval huggingface dataset:
A Context-Based Error Detection Strategy into H.264/AVC CABAC

Various error control schemes have been addressed in wireless video stream transmission. By combining an adaptive binary arithmetic coding technique with context modeling, CABAC as a normative part of H.264/AVC has achieved a high degree of adaptation and redundancy reduction. However, error propagation still remains a problem because of the property of arithmetic coding. The presented scheme compares the various error detection methods, and proposes an efficient error detection technique based on CABAC semantics, which is achieved by inserting detective markers denoting by syntax elements. The misdetection probability versus stream size expansion can be easily handled. In addition, placements of markers can vary with regard to specific video content, thus efficiency within this scheme is enhanced. Comparison with other detection scheme is also presented
Layer-wise supervised neural network for face alignment with multi-task regularization

Convolutional neural networks (CNN) have achieved prominent performance in facial landmark detection in recent years. However, the training of such deep network is non-trivial due to the over-fitting problem caused by the insufficient training data and the diminishing gradients problem occurred in the back-propagation. To address these problems, we propose a multi-task learning framework with supervised neural networks to jointly detect facial landmarks with a set of related tasks. On the one hand, to handle the over-fitting problem, the proposed method takes the advantage of additional task labels to train the model in a multi-task learning fashion to generate a shared feature representation for high-level recognition tasks. On the other hand, in order to tackle the transparency and diminishing gradients problem, the proposed method enforces supervision to the intermediate layers of the network, augmenting the gradient signal propagated from the final layer. Experiments on public benchmarks validate the effectiveness of the proposed method.
A cost minimization with light field in scene depth MAP estimation

This paper proposes a scene depth map generation method based on lens-based light field cameras. In particular, it achieves the functions of the incident rays and the corresponding directional features, which can favor determining the coordinates of candidate space points. The light rays behind the aperture in the 4D light field would be converted into the rays before the aperture with their directions known. The light rays through the aperture center are denoted as reference light rays and keep their directions. With the probability (cost) of each reference light ray in each depth value, we obtain an initial depth map by selecting the depth value with minimum cost. It would be refined via multi-label optimization and weighted median filtering. Experimental results demonstrate the accuracy of the depth map estimated by the proposed method.
Joint Rate Control for Multiple Sequences coding based on H. 264 standard

The objective of joint rate control is to dynamically distribute the channel capacity among video sequences according to their respective complexities, thus a more uniform picture quality and a more efficient utilization of channel capacity are achieved. Most existing approaches are based on MPEG2 coding platform. This paper presents a novel joint rate control scheme for multiple video sequences coding based on H.264 standard. A novel complexity measure that adapts to the characteristics of H.264 video coding is proposed. Experimental results show that the proposed scheme maintains a good balance in picture quality among the sequences as well as within a sequence
Optimal intra coding of HEVC by structured set prediction mode with discriminative learning

This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called structured set prediction model incorporates max-margin Markov network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov network is adaptively derived to maintain the coherence of set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
Redundant multiscale structure coding for error resilient video completion

The fundamental problem associating error resilience with vision-related technique is how to generate a plausible substitute for the unknown regions with complex and semantic structure, implicitly or explicitly by adding significant context redundancy to the coded video, subject to local similarity and global consistence in tempo-spatial domain. Following the structure-aware inpainting for image coding, an adaptive error resilience algorithm using redundant structure coding with multiscale B-spline based feature localization is proposed in this paper. In the proposed scheme, the curvature-based curve representation information extracted from original video pictures is coded and encapsulated into redundant slice of the H.264/AVC standard. To meet the required channel bandwidth and conditions, the underlying redundant structure coding allows for different representations with an optimal coding strategy from corresponding texture compressed version in the primary slice. With the proposed curvature and distortion measure, we can observe that acceptable compressed rate is achieved while progressive reconstruction accuracy is ensured.
Progressive Dictionary Learning with Hierarchical Structure for Scalable Video Coding

To enable learning-based video coding for transmission over heterogenous networks, this paper proposes a scalable video coding framework by progressive dictionary learning. With the hierarchical B-picture prediction structure, the inter-predicted frames would be reconstructed in terms of the spatio-temporal dictionary in a successive sense. Within the progressive dictionary learning, the training set is enriched with the samples from the reconstructed frames in the coarse layer. Through minimizing the expected cost, the stochastic gradient descent is leveraged to update the dictionary for practical coding. It is demonstrated that the learning-based scalable framework can effectively guarantee the consistency of motion trajectory with the well-designed spatio-temporal dictionary.
Joint Source and Flow Optimization for Scalable Video Multirate Multicast Over Hybrid Wired/Wireless Coded Networks

This paper aims to optimize the overall video quality and traffic performance for multi-rate video multicast over hybrid wired/wireless networks. In order to perform layered utility maximization over tiered networks, we propose a joint source-network flow optimization scheme where individual layers of the scalable video stream are imposed on their optimal multicast paths and associated rates for the highest sustainable layered video quality with minimum costs. It sufficiently guarantees that each destination node accesses progressive layered stream in an incremental order, considers network coding across overlapping paths to destination nodes for decent multicast capacity, and addresses the link contention problem during wireless transmission. We formulate the problem into convex programming with the objective to minimize the total rate-distortion variations between layers. Using primal decomposition and the primal-dual approach, we develop a decentralized algorithm with two levels of optimization. The numerical and packet-level results compare extensive performance under different control conditions over coded and non-coded hybrid networks. It demonstrates that the proposed algorithm could actually achieve the max-flow throughput and provide better video quality with optimal layered access for heterogeneous receivers.
Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding

Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.
Optimal Spatio-Temporal Projections with Holo-Kronecker Compressive Sensing of Video Acquisition

In this paper, we proposes a highly compressed video sampling scheme that optimally utilizes the redundancy spanning all the dimensions of the signal. Considering that we further compress the redundant measurements of the existing Kronecker compressive sensing (KCS), the proposed scheme will significantly improve the sampling efficiency. The contribution of our work is two fold. First, a holo-compressive sampling spanning both temporal and spatial dimensions is proposed to overcome the drawback of KCS, and named HKCS. This new scheme keeps on employing Kronecker product to design the sensing matrix. However, it is worth emphasizing that the original identity matrix, a factor of the conventional Kronecker product sensing matrix, is replaced by an ill-posed matrix which enables compressive samplings along the temporal dimension. The advantages of HKCS include: samplings of video along the temporal and spatial dimensions are simultaneously compressed and the necessary measurements for exact recovery are signihcantly reduced; moreover, the Kronecker product sensing matrix retains the block structure, which ensures a feasible distributed sampling scheme for practical application. We prove that, with the same sampling rate, matrices of HKCS have relatively smaller mutual coherence.
Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
Inferring BP Priority Order Using 5D Tensor Voting for Inpainting-Based Macroblock Prediction

In this paper, we propose an optimized in painting-based macro block(MB) prediction mode (IP-mode) in the state-of-the-art H.264/AVC video compression engine, and belief propagation (BP) is applied to achieve the global spatio-temporal consistency between the predicted content and the co-located known region. To decrease the computing complexity of the iterative BP algorithm, we explore structure and motion features by tensor votes projected from the decoded regions, to assign the priority of message scheduling and prune the intolerable labels. No side information is need to be coded into the bit stream, while the structure and motion information is estimated from the decoded region at decoder side. Compared with the existing prediction modes in H.264/AVC, the proposed IP-mode only encode the macro block header and residual data, where the residual is lighter in homogeneous texture regions by the optimized BP algorithm with label pruning. Experiments validate that the proposed video compression scheme can achieve a better R-D performance, and the computing complexity is largely reduced through the inference of structure and motion features.
Depth map estimation with 4D light fields using confocal stereo

We present a scene depth map generation method based on light field cameras. From the plenoptic function, the angular information about each image point under different sizes of aperture is extracted, which could be used for confocal stereo. Considering confocal constancy and gradient constancy, we take into account two constraints: (1) When a pixel is in focus, its relative intensities across aperture should match the variation predicted by the relative exitance of the lens; and (2) When a pixel is in focus, the gradient of the pixel should equal to that of the corresponding pixel in reference image. Based on these two constraints, we develop data term which measures the probability of each pixel in each depth. Considering the textureless area, we also develop smoothness term which helps to determine the depth of textureless area by its neighboring texture area. Finally, the depth map is estimated via multi-label optimization and weighted median filtering.
Dynamic rate allocation and opportunistic routing for scalable video multirate multicast over time-varying wireless networks

In this paper, we consider the time-varying characteristics of practical wireless networks, and propose a joint dynamic rate allocation and transmission scheduling optimization scheme for scalable video multi-rate multicast based on opportunistic routing (OR) and network coding. With OR, the decision of optimal routes for scalable video coding (SVC) layered streaming is integrated into the joint optimization formulation. The network throughput is also increased by taking advantage of the broadcast nature of the wireless shared medium and by network coding operations in intermediate nodes. To maximize the overall video reception quality among all destinations, the proposed scheme can jointly optimize the video reception rate, the associated routes to different destinations, and the time fraction scheduling of transmitter sets that are concurrently transmitting in the shared wireless medium. By using dual decomposition and primal-dual update approach, we develop a cross-layer algorithm in a fully distributed manner. Simulation results demonstrate significant network multicast throughput improvement and adaptation to dynamic network changes relative to existing optimization schemes.
Kernel matched subspace detectors for hyperspectral target detection

In this paper, we present a kernel realization of a matched subspace detector (MSD) that is based on a subspace mixture model defined in a high-dimensional feature space associated with a kernel function. The linear subspace mixture model for the MSD is first reformulated in a high-dimensional feature space and then the corresponding expression for the generalized likelihood ratio test (GLRT) is obtained for this model. The subspace mixture model in the feature space and its corresponding GLRT expression are equivalent to a nonlinear subspace mixture model with a corresponding nonlinear GLRT expression in the original input space. In order to address the intractability of the GLRT in the feature space, we kernelize the GLRT expression using the kernel eigenvector representations as well as the kernel trick where dot products in the feature space are implicitly computed by kernels. The proposed kernel-based nonlinear detector, so-called kernel matched subspace detector (KMSD), is applied to several hyperspectral images to detect targets of interest. KMSD showed superior detection performance over the conventional MSD when tested on several synthetic data and real hyperspectral imagery.
Very-low-bit-rate video coding using quadtree decomposition and cache-based vector quantization

We investigate the use of vector quantizers (VQs) with memory to encode image sequences. A video coding technique using cache-based vector quantization is presented. In this technique, a small codebook (subcodebook), meaning a cache memory codebook, is used to encode the input signals. This small subcodebook is dynamically generated from a much larger codebook (supercodebook) through a caching scheme. Therefore, the subcodebook dynamically adapts to the local characteristics of the motion-compensated residual signal, which varies with time. Both "least recently used" (LRU) and modified LRU cache-replacement techniques are used. An efficient bit-allocation strategy using quadtree decomposition is used with the cache-based VQ to compress the video signal. The proposed video codec outperforms H.263 in terms of PSNR and perceptual quality at very low bit rates, ranging from 5 to 15 kbps. Experimental results are presented for two image sequences, "salesman" and "Miss America".
Automated Hyperspectral Cueing for Civilian Search and Rescue

Hyperspectral remote sensing provides information related to surface material characteristics that can be exploited to perform automated detection of targets of interest and has been applied to a variety of remote sensing applications. This paper explores the application to civilian search and rescue, using the airborne real-time cueing hyperspectral enhanced reconnaissance (ARCHER) system developed for the civil air patrol as a key example of how evolving hyperspectral technology can be employed to support these operations. ARCHER combines a visible/near-infrared hyperspectral imaging system, a high-resolution visible panchromatic imaging sensor, and an integrated geopositioning and inertial navigation unit with onboard real-time processing for data acquisition and correction, precision image georegistration, and target detection and cueing. Processing for detecting downed aircraft wreckage and other related objects employs real-time adaptive anomaly detection and matched filtering algorithms, and a non-real-time change detection mode to provide further false alarm reduction in some instances. This paper describes the system technology, with an emphasis on the current and evolving automated target detection methods, and summarizes the operational experience in the airborne employment against civilian search and rescue missions.
Penalized spectral matched filter for target detection in hyperspectral imagery

This paper describes a new adaptive spectral matched filter that incorporates the idea of regularization (shrinkage) to penalize and shrink the filter coefficients to a range of values. The regularization has the effect of restricting the possible matched filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral matched filters. The effect of regularization depends on the form of the regularization term and the amount of regularization is controlled by so called regularization coefficient. In this paper the sum-of-squares of the filter coefficients is used as the regularization term and several different values for the regularization coefficient are tested. Experimental results for detecting targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters.
A non-linear predictor for differential pulse-code encoder (DPCM) using artificial neural networks

A nonlinear predictor is designed for a DPCM encoder using artificial neural networks (ANN). The predictor is based on a multilayer perceptron with three input nodes, 30 hidden nodes and one output node. The back-propagation learning algorithm is used for the training of the network. Simulation results are presented to evaluate and compare the performance of the neural net based predictor (nonlinear) with that of an optimized linear predictor. Success in the use of the nonlinear predictor is demonstrated through the reduction in the entropy of the differential error signal as compared to that of a linear predictor. Also it is shown that the ANN predictor is much more robust for encoding noisy images compared to that of a linear predictor.<<ETX>>
Object recognition based on graph matching implemented by a Hopfield-style neural network

A model-based object recognition technique is presented. For each model, distinct features such as curvature points are extracted, and a graph consisting of a number of nodes connected by arcs is constructed. Therefore, each node in the graph represents a feature that has an assigned feature type and a numerical feature value, and an arc between two nodes shows the relationship or compatibility between the features such as distances between feature points. Objects recognition is formulated as matching a model graph with an input image graph. A Hopfield binary network is implemented to perform a subgraph isomorphism to obtain optimum compatible matching features between graphs. The compatibility is defined so that it will tolerate the ambiguity of preprocessed features. The algorithm is also extended to detect one object among several objects which could be touching or overlapping. Some simulation results are shown to evaluate the performance of the system.<<ETX>>
SAR moving target detection and identification using stochastic gradient techniques

This paper presents methods for detecting and identifying moving targets in a synthetic aperture radar (SAR) scene. An analytical expression is derived for the coherent SAR signature of a target. SAR system model of a moving target is developed. These principles are then used to construct a SAR signal statistic (energy function) in a parameter space which is defined by the target's coordinates, speed, and coherent SAR signature. Stochastic gradient techniques are used to search for the maximum point of this energy function which is located at the desired target's parameters.