nils-herrmann/clean_scirepeval huggingface dataset:
Distributed rate and power allocation for wireless video chats via pricing schemes

Video chat is a power and rate-intensive application which requires efficient resource utilization. Unlike video streaming which is generally one way, video chats characterize distributed two way traffics relayed via base stations. In this paper, we propose a distributed rate and power allocation framework for joint coding and transmission in wireless video chats. The base station imposes a service charge, which considers relay transmission power as a cost, for relaying video bitstreams. For clients, we derive the optimal rate and power allocation for video coding and transmission such that the network service charge and video distortion are minimized under a power constraint. For the base station, existing pricing schemes could not ensure fairness and efficiency simultaneously. We propose an optimal hybrid pricing scheme which allows balanced tradeoff between fairness and efficiency in network service. Network dynamics of video chats can be analyzed in the Stackelberg game framework, and shown to converge to the Stackelberg equilibrium. Extensive simulations confirm the performance analysis of the proposed solutions and the network dynamics.
A new method for camera motion parameter estimation

We derive a six parameter system to estimate and compensate the effects of camera motion-zoom, pan, tilt and swing. As compared to other existing methods, this model describes more precisely the effect of different kinds of camera motions. A recursive least-squares estimator has been used to solve for the motion parameters. Experiments suggest that our algorithm converges to satisfactory results when about 10 pairs of corresponding pairs between two image frames are available.
Adaptive downsampling/upsampling for better video compression at low bit rate

To transmit video contents over limited bandwidth network, video bitstreams may need to reduce the bit rate by encoding with coarse quantization parameters at the expense of degrading quality. At low bit rates, better coding quality can be achieved by downsampling the video prior to compression and upsampling later after decompression. In this paper, we present an adaptive downsampling/upsampling video coding scheme in order to achieve better video quality at low bit rates in terms of both measure and visual quality. In particular, appropriate downsampling directions/ratios and quantization step sizes are adaptively decided for encoding different regions of video frame with the consideration of local contents. Experimental results have shown the better performance of the proposed scheme over the regular coding and downsampling-based coding scheme with fixed downscaling ratio. In addition, the proposed scheme significantly raises the critical bit rate below which a downsampling-based coding scheme outperforms the regular coding.
Edge-preserving image denoising via optimal color space projection

Denoising of color images can be done on each color component independently. Recent work has shown that exploiting strong correlation between high-frequency content of different color components can improve the denoising performance. We show that for typical color images high correlation also means similarity, and propose to exploit this strong intercolor dependency using an optimal luminance/color-difference space projection. Experimental results confirm that performing denoising on the projected color components yields superior denoising performance, both in peak signal-to-noise ratio and visual quality sense, compared to that of existing solutions. We also develop a novel approach to estimate directly from the noisy image data the image and noise statistics, which are required to determine the optimal projection
Effective use of spatial and spectral correlations for color filter array demosaicking

To minimize cost and size, most commercial digital cameras acquire imagery using a single electronic sensor (CCD or CMOS) overlaid with a color filter array (CFA) such that each sensor pixel only samples one of the three primary color values. To restore a full-color image from CFA samples, the two missing color values at each pixel need to be estimated from the neighboring samples, a process that is commonly known as CFA demosaicking or interpolation. In this paper we present two contributions to CFA demosaicking. First, we stress the importance of well exploiting both image spatial and spectral correlations, and characterize the demosaicking artifacts due to inadequate use of either correlation. Second, based on the insights gained from our empirical study, we propose effective schemes to enhance two existing state-of-the-art demosaicking methods. Experimental results show that our enhanced methods achieve notable improvements over the existing methods, in terms of both subjective and objective evaluations, on a large variety of test images. In addition, the computational complexities of the enhanced methods are comparable to the originals.
Retinal Vessel Detection using Self-Matched Filtering

Automated analysis of retinal images usually requires estimating the positions of blood vessels, which contain important features for image alignment and abnormality detection. Matched filtering can produce the best results but is difficult to implement because the vessel orientations and widths are unknown beforehand. Many researchers use Hessian filtering, which provides an estimate for vessel orientation through the use of three orientation templates. We propose a novel filtering approach, called self-matched filtering, which is based on the 180deg rotated version of the noisy vessel signal in the local neighborhood. We show that even though the proposed filter achieves half the signal-to-noise ratio of a matched filter, it does not require the estimation of the vessel scale and orientation, and can outperform Hessian filtering by up to a factor of two in terms of miss detection error.
Sports video analysis and structuring

We propose a new method for structuring and analyzing sports video through clustering of video shots based on low-level visual content. The dominant scenes of a sports video are first automatically extracted by using the color features derived from its video shots. The video shots belonging to each dominant scene are then processed to identify interesting play events in the sports video. To reduce the influence of threshold selection on the results, different forms of shot color features, including shot color histogram and its principal component and subspace linear discriminant representations, are also examined. Experimental results on various kinds of sports videos, such as tennis, volleyball, basketball and football videos, are presented to demonstrate the effectiveness of the proposed method.
Adaptive Filtering for Color Filter Array Demosaicking

Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and 2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.
Efficient video retrieval using shot clustering and alignment

Retrieving similar video clips from large video database requires a fast methods that can attain high precision and recall. This paper adopts clustering to speed up the search in database, and proposes a new distance measure, which aligns two video clips to improve the accuracy of their similarity. Based on the clustering and the distance measure, we propose a new algorithm to construct candidate video clips in video query by example. Experimental results show that the proposed video retrieval method is efficient.
A low complexity H.263 to H.264 transcoder

A low complex H.263 to H.264 P-frame transcoder is presented in this paper. The new scheme uses an early-stop skip-mode process, a motion vector refinement algorithm and a simplified inter-mode decision module. Experiment results show that the proposed transcoding scheme can achieve a remarkable reduction of computational complexity at the cost of a negligible rate-distortion loss (under 0.6 dB) as compared with the full transcoder
Video segmentation based on sequential change detection

In content-based video analysis, substantial research efforts have been focused on developing techniques to detect the boundaries between two successive shots, each comprising consecutive frames filmed with a single camera act. However, there is still room for further improvement in the detection performance. With the use of sequential change detection and the help of nonparametric density estimation principles, we propose A new shot boundary detection method that can maintain not only satisfactory detection accuracy, but also consistent detection performance based on the results of various test videos.
Enhanced face recognition using tensor neighborhood preserving discriminant projections

We propose in this paper a novel subspace learning method called tensor neighborhood preserving discriminant projections (TNPDP) for face recognition. Compared with the conventional appearance-based face recognition method, the proposed TNPDP does not need to perform image-to-vector conversion and can well preserve the structure of the original image. Different from the existing tensor-based recognition approaches such as tensor subspace analysis (TSA) and discriminant analysis with tensor representation (DATER), TNPDP considers locality and discriminative information simultaneously and can find the optimal tensor subspace that best maintains locality neighborhood manifold and discriminates different classes by maximizing the between-class scatter while minimizing the within-class scatter. Experimental results on two benchmark face databases demonstrate the effectiveness of the proposed method and indicate that TNPDP is better than TSA and DATER, as well as other popular face recognition methods such as principal component analysis (PCA) and linear discrimination analysis (LDA).
On the methods and applications of arbitrarily downsizing video transcoding

Video transcoding is a common technique for adapting the bitrate or spatial/temporal resolution of a compressed video to suit different transmission bandwidths or receiving devices. To reduce the computational complexity, many fast methods have been proposed to estimate the motion vectors required for downsizing a pre-coded video by an integer factor. We develop and compare several fast video transcoding methods for downsizing a pre-coded video by an arbitrary factor Methods which out-perform others under different conditions are identified and discussed. To exploit fully the advantages of arbitrarily downsizing video transcoding, we also design a scheme to determine the reduced frame size that can sustain the best possible video quality for a given target bitrate. Experimental results are presented to show the performance of the proposed video transcoding methods.
Enhancing incremental learning/recognition via efficient neighborhood estimation

One of problems with incremental learning approaches is that the quality of learning samples cannot be controlled since they are provided on-line. Current incremental learning algorithms ignore the issue by accepting each sample unconditionally and assimilating it into the existing recognition system. However, improper sample, caused by a partial occlusion, badly illumination condition and unusual pose, deteriorates the overall performance of the recognition system significantly. In order to overcome this issue, in this paper, based on hypothesis and verification paradigm, we propose a criterion for sample selection, and present a novel system for automatically evaluating the qualities of new added samples during the incremental learning procedure. Following this, a subspace is learned by using only the samples that are considered to be good after verification. The high computation cost, incurred by inclusion of sample verification into incremental learning framework, is greatly reduced using relative distance filtering. The experimental results demonstrated the superiority of our approach, in both recognition accuracy and robustness.
Numerical error analysis for super-resolution reconstruction

Superresolution reconstruction (SR) is a technique for reconstructing a high resolution image from several low resolution ones. Recently, some researchers study the relationship of SR error and magnification factor, and give out the efficient and forbidden magnification range in their published literature. Based on their work, we describe SR error bound in more explicit way and reveal the essential factor affecting SR performance, if infinite low resolution (LR) observations are given. For SR implementation, smooth regularization serves as an efficient tool for producing stable SR solution, but this operator degrades SR performance and produces overly smooth SR image at certain magnifications. To show its mechanism, we augment SR model by integrating regularization operator into it, and then present the curve of SR error bound vs magnification factors. It is proven that the forbidden magnification range no longer exists when the regularization is involved.
Deep transfer metric learning

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML where the output of both the hidden layers and the top layer are optimized jointly. Experimental results on cross-dataset face verification and person re-identification validate the effectiveness of the proposed methods.
Doubly weighted nonnegative matrix factorization for imbalanced face recognition

We propose in this paper a novel doubly weighted nonnegative matrix factorization (DWNMF) method for imbalanced face recognition. Motivated by the fact that some face samples and certain parts of each face sample are more useful for recognition, we construct two weighted matrices based on the pairwise similarity of face samples in the same class and the discriminant score of each face pixel. Compared with the existing NMF algorithm, the proposed DWNMF method can more effectively exploit the discriminative and geometrical information of face samples, and it is especially suitable for imbalanced face recognition. Experimental results are presented to demonstrate the efficacy of the proposed method.
Channel Access Allocation for Scalable Video Transmission Over Contention-Based Wireless Networks

Contention-based wireless networks make use of random access delay in the medium access control (MAC) layer to share the channel efficiently among users. This channel access scheme poses challenges to video traffic which is delay-sensitive. When channel access is limited, how to optimally allocate channel access and judiciously drop video packets remains an open issue. In this paper, we investigate the optimal channel access allocation and preemptive packet drop strategy for scalable video packets using contention-based MAC. We first establish a relationship to associate the characteristics of video packets, the cumulative distribution function of channel access delays and the expected video quality at the receiver. Based on the relationship, we define a video utility function that explicitly takes into account cumulative distribution of channel access delay over contention-based wireless links, video packet length and truncation, packet loss rate and quality impacts of SVC layers. As the analytical model of the video utility function is rather complex for optimization, we further propose an approximation model for the video utility function to transform the optimization problem into a convex problem which is a solvable by using the dual decomposition method. Simulation results confirm that our proposed method achieves better PSNR performance over the existing approach, especially when the total channel access for the video traffic is limited.
Video Summarization Via Multiview Representative Selection

Video contents are inherently heterogeneous. To exploit different feature modalities in a diverse video collection for video summarization, we propose to formulate the task as a multiview representative selection problem. The goal is to select visual elements that are representative of a video consistently across different views (i.e., feature modalities). We present in this paper the multiview sparse dictionary selection with centroid co-regularization method, which optimizes the representative selection in each view, and enforces that the view-specific selections to be similar by regularizing them towards a consensus selection. We also introduce a diversity regularizer to favor a selection of diverse representatives. The problem can be efficiently solved by an alternating minimizing optimization with the fast iterative shrinkage thresholding algorithm. Experiments on synthetic data and benchmark video datasets validate the effectiveness of the proposed approach for video summarization, in comparison with other video summarization methods and representative selection methods such as K-medoids, sparse dictionary selection, and multiview clustering.
On model-based clustering of video scenes using scenelets

We propose in this paper a model-based approach to clustering video scenes based on scenelets. We define a video scenelet as a short consecutive sample of frames of a video sequence. The approach makes use of an unsupervised method to represent scenelets of a video with a concise Gaussian mixture model and cluster them into different video scenes according to their visual similarities. In particular the expectation-maximization algorithm is employed to estimate the unknown model parameters, and Bayesian information criterion is used to determine the optimal number and model of scene clusters in a principled manner. This approach is fundamentally different from many existing video clustering methods, as it does not require explicit knowledge of shot boundaries. Instead, the shot boundaries can also be obtained as a by-product of the scene clustering process. The proposed methods have been tested with various types of sports videos and promising results are reported in this paper.
Complexity-scalable video coding and power-rate-distortion modeling forwireless video chat applications

Wireless video chat is a power-consuming and of high bitrate application. To prolong the operational lifetime, optimal rate and power allocations in joint coding and transmission are necessary. We exploit low motion and high inter-frame correlation in video chats to determine a complexity-scalable video coding adaptation which is Pareto optimal. We propose a model that describes power-rate-distortion (PRD) characteristic of the complexity scalable video coding more accurately. As video contents are non-stationary, we formulate an online algorithm for the model's parameters updates. We demonstrate that the PRD model with online updates can be applied to solve the rate and power allocation problem optimally in joint coding and transmission. Simulation results confirm that the model describes PRD characteristics more accurately via online recursive updates. The resource allocation scheme which is based on the PRD model yields better video quality than recent methods in a resource-constrained wireless video chat application.
Energy-minimized wireless video transmissions via sharing of retransmission limits

In wireless video streaming, a video frame is usually large in data load and is truncated into multiple transport packets for reliable transmissions. These transport packets should be transmitted successfully before a deadline for frame decoding. High bitrate and high power transmission schemes are often deployed to ensure reliable transmissions. Such a method however incurs substantial energy consumption. Unlike existing methods which assign the retransmission limit independently to each transport packet, we share the retransmission opportunity among transport packets of a video frame, and jointly optimize it with transmission rate and power such that the video frame delivery is energy efficient. Contrary to the common intuition that avoids packet loss and retransmission to preserve energy, our approach yields lower energy consumption by judiciously inducing loss and retransmission of the transport packets, without violating the deadline and reliability constraint. Numerical results reveal that the joint optimization improves energy efficiency in wireless video transmissions. The improvement is even more significant when the allocated transmission time is limited or the reliability constraint is tightened.
Robust Point Set Matching for Partial Face Recognition

Over the past three decades, a number of face recognition methods have been proposed in computer vision, and most of them use holistic face images for person identification. In many real-world scenarios especially some unconstrained environments, human faces might be occluded by other objects, and it is difficult to obtain fully holistic face images for recognition. To address this, we propose a new partial face recognition approach to recognize persons of interest from their partial faces. Given a pair of gallery image and probe face patch, we first detect keypoints and extract their local textural features. Then, we propose a robust point set matching method to discriminatively match these two extracted local feature sets, where both the textural information and geometrical information of local features are explicitly used for matching simultaneously. Finally, the similarity of two faces is converted as the distance between these two aligned feature sets. Experimental results on four public face data sets show the effectiveness of the proposed approach.