nils-herrmann/clean_scirepeval huggingface dataset:
A cross-layer adaptation HCCA MAC for QoS-aware H.264 video communications over Wireless Mesh Networks

We present in this paper a novel scheme of QoS-guaranteed transmission of H.264 video over Wireless Mesh Networks (WMNs) based on a Cross-Layer Adaptation HCCA (CLA-HCCA) MAC protocol. This CLA-HCCA strategy utilizes the queue length information at the MAC layer and the MAC layer interference estimation for optimal routing selection at network layer. The scheme will exchange timely Link Capacity Estimation (LCE) information for an adaptive adjustment of spectrally capable transmission at the physical layer. This Cross-Layer Adaptation approach provides an optimized transmission to guarantee the minimum packets delay and drop rate needed for time bound applications, such as video. Since conventional IEEE 802.11e HCCA MAC does not offer cross-layer design framework, we need to resolve the problem associated with IEEE 802.11e HCCA MAC by designing a novel adaptive architecture based on LCE. This integrated scheme allows the physical layer to achieve the optimal transmission via Video-Adaptive FEC (VA-FEC) scheme implemented in the application layer. We evaluate the proposed scheme based on network-level metrics, including bit rate, packets delay and drop rate, in comparison with the state-of-the-art Physical Rate Based Admission Control scheme (PRBAC) HCCA MAC scheme based on IEEE 802.lie WMNs. Extensive simulation results based on H.264 video transmission for the proposed CLA-HCCA and the PRBAC-HCCA schemes have been obtained. We have confirmed that the proposed CLA-HCCA strategy outperforms the PRBAC-HCCA MAC by a significant margin as LCE and VA-FEC is able to adapt to the dynamic network conditions of IEEE 802.lie WMNs in a timely fashion.
Left ventricle global motion and shape from CT volumetric data

The authors address the problem of estimating left ventricle (LV) global motion and shape from CT (computed tomography) volumetric data based on surface modeling. An improved approach able to estimate the global motion and deformation as well as to construct a parametrized global shape of the LV has been developed. The global rigid motion of the LV is computed from the position and orientation change of an estimated time-varying and object-centered coordinate system, while the global deformations are obtained through the fitting of global shape modeling primitives to the given digital volumes of the LV. Major improvements over the initial effort (C.W. Chen and T.S. Huang, 1992) include a robust algorithm for the LV long axis estimation and the implementation of weighted surface fitting based on the original image statistics of the boundary points.<<ETX>>
Secure media sharing in the cloud: Two-dimensional-scalable access control and comprehensive key management

Media sharing in cloud environment, which supports sharing media content at any time and from anywhere, is a promising paradigm of social interaction. However, it also brings forth security issues in terms of data confidentiality and access control on media data consumers with different access privileges. One promising solution is scalable media access control, which is capable of providing data confidentiality by encrypting the media data and issuing the key to only authorized data consumers. More importantly, it can empower the data distributor to provide the same media content with various quality levels to the consumers with different privileges. Traditional schemes without utilizing the cloud resources achieve scalable media access control by generating access keys using hash chains. Despite of their computational efficiency, such schemes suffer from various problems including vulnerability to user collusion attack in two-dimensional case, inflexible key distribution, and ambiguous key revocation strategy. In this paper, we propose a novel two-dimensional-scalable access control by generating access keys based on Attribute-Based Encryption (ABE) algorithm. Moreover, the proposed scheme can efficiently achieve comprehensive key management including key distribution and key revocation by fully exploiting the cloud. Security analysis shows that the proposed scheme is able to provide collusion resistance, as well as forward and backward secrecy. We have also evaluated the efficiency of the scheme through numerical analysis and initial implementation.
A two-layer and multi-strategy framework for human activity recognition using smartphone

Human Activity Recognition (HAR) is widely used in many applications and HAR using smartphone only has been proved to be effective, flexible and unobtrusive for activity recognition. In this paper, a two-layer and multi-strategy HAR framework is proposed to overcome the major challenge of HAR using smartphone only, i.e., the variation in orientation and position of the device. In the first layer, the activities are classified into different groups with high accuracy and for each group in the second layer, the appropriate strategy is designed according to the characteristics of the group to improve the recognition performance. For static activity group, the transitional activities are introduced to help classifying the activities indirectly. For dynamic activity group sensitive to the position variation of the smartphone, a position-assisted strategy is proposed to alleviate the influence of position variation. The simulation results demonstrate the effectiveness of the proposed two-layer multi-strategy HAR framework.
Robust Transmission of Scalable Video Coding Bitstream Over Heterogeneous Networks

Video transmission using Scalable Video Coding (SVC) enables the functionalities of adaptation for both channel bandwidth and terminal device. However, link adaptation, which requires the video bitstream to provide error resilient scalability (ERS), is currently not supported in SVC. In this paper, we propose a robust SVC bitstream transmission framework, which integrates both error resilient (ER) coding and error concealment (EC) to enable the desired link adaptability. First, at the encoder, we design an ER coding scheme for SVC using redundant pictures, in which redundant picture information (RPI) is generated under rate-distortion criteria and transmitted to the media gateway together with the SVC bitstream. Then, ERS can be effectively achieved at the media gateway by selectively adding or removing coded pictures of different video coding layers according to the RPI and current network link status. Finally, at the decoder, we take advantage of the unique characteristics of SVC to develop an EC scheme based on both the proposed Virtual-BLSkip method and Wiener filtering to recover the lost or removed enhancement layer pictures. The proposed framework has negligible impact on the coding efficiency because it transmits RPIs rather than directly adding redundancy into the original bitstream. More importantly, it successfully strikes a balance between coding efficiency, error resiliency, and operation complexity. It is capable of providing the desired ERS with extremely low complexity, and maintaining virtually the same bit rate as input video bitstream. The experimental results show that the proposed framework achieves significant performance gains over existing ER transmission schemes.
Segmentation and features extraction techniques, with applications to biomedical images

In order to obtain a 3-D reconstruction of the hippocampus from a volumetric MRI head study, it is necessary to separate it not only from the surrounding white matter, but also from contiguous areas of gray matter. At present it is necessary for a physician to manually segment the hippocampus on each slice of the volume in order to obtain such a reconstruction. The authors propose a novel technique by which a computer may make an unsupervised identification of a given structure through a series of images, even if that structure includes so-called false contours or missing contours. Applications include 3-D reconstruction of difficult-to-segment regions of the brain and abdomen, and volumetric measurements of organs from series of 2-D images.<<ETX>>
Mobile Video Perception: New Insights and Adaptation Strategies

It is now a common knowledge for mobile users to notice that the video viewing experience for the same device and same content can be dramatically different. The main reason for such difference is the dynamic viewing environment of mobile device which may change from indoor to outdoor or from walking to riding in a traveling vehicle. In this paper, we report our recent study on how mobile video perception is different from conventional TV viewing experiences in the living room and on which adaption strategy we can take to generate video bitstreams that matches better with the dynamic viewing environment. We first identify several key factors that would impact the mobile video perception, including display size and viewing distance, surrounding luminance, and body movement. These factors will be evaluated by context-aware subjective tests specifically designed to quantify the correlations between these factors and the viewing experience. Based on such evaluation, we develop a mobile video quality assessment tool to measure the just noticeable distortion in a given viewing environment. With appropriate rate adaptation to viewing context parameters, we show that an environment-aware video bit-stream preparation is able to enhance mobile video experience by exploiting inherent video signal redundancy under a given viewing context. Such adaptation can also reduce the bitrate requirement by an average of 30%.
Left ventricle motion analysis by hierarchical decomposition

An approach to the modeling and analysis of left ventricle motion and deformation is presented. The hierarchical motion model of the left ventricle is constructed by combining several simple models and is able to capture major motion and deformation components that are already confirmed by medical observations. The hierarchical decomposition characterizes the left ventricle motion and deformation in a coarse-to-fine fashion and leads to computationally efficient estimation algorithms. The global rigid motion of the left ventricle is estimated by establishing a time-varying object-centered coordinate system. The global and local deformations of the left ventricle are obtained by fitting the given data to superquadric and spherical harmonic modeling primitives, respectively. The animation of estimation results is consistent with prior knowledge of the left ventricle and therefore shows the success of the hierarchical decomposition based approach.<<ETX>>
Analog Coded SoftCast: A Network Slice Design for Multimedia Broadcast/Multicast

This paper presents a network slice design for ultra high definition (UHD) video broadcast/multicast to achieve higher network efficiency and improved quality of experience (QoE). The proposed network slice design consists of a rateless source compression scheme and an analog-coded SoftCast scheme. The rateless Spinal code is adopted to compress the video source at content server and the compressed source is transmitted from content server across wireless core network to the base station. An a priori information-assisted Spinal decoder is designed to utilize the sparsity of bit planes for compression. In the analog-coded SoftCast scheme, we design a new chaotic function-based analog code with negligible power penalty for the generalized Gaussian-distributed source in SoftCast because the existing chaotic functions designed for uniformly distributed sources suffer from serious power penalty in SoftCast. We also design a maximum a posteriori probability decoding algorithm for the proposed analog code in order to exploit the statistics of video source as a priori information to improve the performance. The experimental results show that the proposed rateless code-based compression scheme achieves efficient compression and approaches the bound of binary erasure channel. In particular, the 1/2 analog-coded SoftCast has almost 2 dB gain over conventional SoftCast with two repetitions, and the 1/3 analog-coded SoftCast has almost 3 dB gain over conventional SoftCast with three repetitions. The system simulations for the broadcast system show higher network capacity and improved QoE in the proposed UHD slice, because the reconstructed video quality of each user is commensurate with its channel condition.
Proactive Interference Avoidance for Mobile-to-Mobile Communication in LTE Networks

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme.
Face location in wavelet-based video compression for high perceptual quality videoconferencing

We present a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. In addition to an adaptive quantization in which spatial constraints are enforced to preserve perceptually important information at low bit rates, semantic information of the human face is incorporated to design a hybrid compression scheme for videoconferencing, since the face is often the most important part and should be coded with high fidelity. The human face is detected based on contour extraction and feature point analysis. An approximate face mask is then used in the quantization of the decomposed subbands. At the same total bit rate, coarser quantization of the background enables the face region to be quantized finer and coded with a higher quality. Moreover, the resultant larger quantization noise in the background can be suppressed using an edge-preserving enhancement algorithm. Experimental results have shown that the perceptual image quality is greatly improved using the proposed scheme.
Wireless Video Streaming QoS Guarantees Based on Virtual Leaky Bucket

With recent rapid advances in wireless communication and networking, the demand for wireless video streaming is exploding. Unlike data streaming, video streaming is much more challenging due to unique characteristics such as bursty flow, stringent delay bound, and selective loss tolerance. Under the dynamic nature of wireless channels, wireless video streaming is virtually impossible to achieve deterministic quality-of-service (QoS) guarantee. Recent development in statistical QoS guarantees has shown great potential for wireless video streaming. However, in the case of statistical QoS guarantee, loss of data due to queue length bound violation or delay violation is often inevitable. Because of unequal importance of video data stream and their inherent dependency, different loss patterns will result in distinctive loss impact. It is critical to design an appropriate scheme to control the loss of video data in such a way that it will minimize the impact of such loss. We develop in this research a prioritized packet dropping scheme based on virtual leaky bucket to control the delivery of video streams. We have carried out analysis based on effective capacity, effective bandwidth, and virtual leaky bucket to demonstrate the potential of the proposed scheme. Preliminary results obtained from simulations show that an improved performance in QoS guarantees can be achieved for wireless video streaming.
A Unified Scheme for Super-Resolution and Depth Estimation From Asymmetric Stereoscopic Video

Reconstructing a full-resolution stereoscopic video from an asymmetric stereoscopic video is a challenging task. The existing approaches require depth information, which imposes an additional challenge in data acquisition. In this paper, we propose a novel scheme that is capable of obtaining super-resolution and depth estimation simultaneously from an asymmetric stereoscopic video. The proposed scheme models the video super-resolution and stereo matching with a unified energy function. Then, we apply an alternating optimization method to minimize this energy function, which can be implemented with a two-step algorithm. In the first step we calculate the initial depth map by using a region-based cooperative optimization technique while considering the temporal consistency in video. In the second step we resolve the super-resolution problem under the guidance of the depth information. It is effective because each step benefits from the additional improvement over the previous step. We iteratively update the two steps until stable depth and super-resolution results are obtained. We have conducted a series of experiments on public stereoscopic video sequences to evaluate the performance of the proposed method. Both objective indexes and subjective visual comparisons verify that the proposed scheme can achieve satisfactory super-resolution results and high-quality depth map simultaneously. In particular, the subjective evaluation experiments on a 3-D monitor show that this scheme outperforms others and achieves the best visual sharpness.
Adaptive Hybrid Digital–Analog Video Transmission in Wireless Fading Channel

We propose an adaptive hybrid digital-analog video transmission (A-HDAVT) scheme for robust video streaming in mobile networks with realistic fading channels. This scheme is fundamentally different from recent research in hybrid digital-analog video transmission in which wireless channels are unrealistically assumed to be Gaussian. For fading channels, it is critical to take full advantage of diversity in both video contents and multiuser channels. Like all hybrid approaches, A-HDAVT is designed to exploit the benefits from both digital and analog systems. To achieve this goal, each group of pictures is first transformed into one low-pass frame and several high-pass frames with motion-compensated temporal filtering. The critical low-pass frame is reliably transmitted as base layer in a digital mode, while high-pass frames are transmitted as enhancement layers in an analog mode to achieve desired graceful degradation performance. In analog transmission, we introduce a channel prediction-based adaptive power-distortion optimization (P-APDO) scheme to combat channel fading in mobile networks. The basic idea behind P-APDO is to perform power allocation based on the video content as well as the predicted channel status. Furthermore, we also investigate the multiuser scenarios in which the content diversity and channel diversity among users are appropriately exploited. Extensive simulations have been carried out to evaluate the performance of A-HDAVT under various degrees of channel fading. The results show that A-HDAVT achieves significant performance gains over competing schemes Robust Uncoded Video Transmission, Parcast, and Softcast in both single-user and multiuser scenarios.
Local Co-Occurrence Selection via Partial Least Squares for Pedestrian Detection

Channel feature detectors are the most popular approaches for pedestrian detection recently. However, most of these approaches train the boosted decision trees by selecting a single feature at each node, which does not effectively exploit the multi-feature cues and spatial information. To address this issue, this paper proposes to construct the co-occurrence of multiple channel features in local image neighborhoods for pedestrian detection. In our approach, a binary pattern of feature co-occurrence is represented by combining the binary variables quantized from each channel feature, and the spatial information is incorporated by selecting the neighbors to jointly represent the feature co-occurrence in a local image block. However, feature co-occurrence selection leads to many possible feature combinations, which significantly increase the computational cost at the training stage. Therefore, in order to reduce the number of candidate features and obtain the most discriminative features effectively, a partial least squares-based feature selection approach called variable importance on projection is exploited. Comprehensive experiments are conducted on several challenging pedestrian data sets, and superior performances are achieved by the proposed approach in comparison with some state-of-the-art pedestrian detection approaches.
Semantic adaptation of consumer photo for mobile device access

This paper proposes a novel event-aware semantic image adaptation framework for accessing consumer photos via mobile devices. There are two major challenges to develop such an image adaptation framework: (1) how to bridge the semantic gap in the search for the desired photos from a consumer database; (2) how to perform appropriate adaptation for various small displays in order to maximize user's perceptual experience according to the semantic significance of objects in photos. To meet the first challenge, an event semantics-guided extraction is developed based on statistical fusion of bottom-up low level features and top-down semantic features. To meet the second challenge, a key object-based semantic adaptation approach is designed to obtain the perceptually optimized Region of Interest (ROI). We conducted experiments based on the events defined in the Kodak consumer photo database . These experiments show that by utilizing the semantics of the photos with event-based a priori knowledge, the adaptation result outperforms the conventional attention based scheme. More importantly, the proposed framework can be easily extended to other events by adopting the event semantics based a priori knowledge into the adaptation system.
Authenticating Multimedia Transmitted Over Wireless Networks: A Content-Aware Stream-Level Approach

We propose in this paper a novel content-aware stream-level approach to authenticating multimedia data transmitted over wireless networks. The proposed approach is fundamentally different from conventional authentication methods and offers robust authentication for multimedia data in the presence of channel noise. The scheme is designed in such a way that it facilitates explicit capture and exploitation of channel condition as well as how the multimedia content is packetized and transmitted. The design allows the integration of authentication with the framework of joint source and channel coding (JSCC) to achieve adaptiveness to the content and efficient utilization of limited bandwidth. We have realized the proposed scheme through optimal resource allocation and authentication graph construction. Experiment results demonstrated the effectiveness of this novel approach