nils-herrmann/clean_scirepeval huggingface dataset:
Portal image feature extraction by hierarchical region processing technique

We describe a novel hierarchical region processing technique for the extraction of features from portal images. Feature extraction is an essential component in the computerized registration of portal images for radiation treatment verification and patient setup error minimization. Unlike the existing methods with global processing techniques, the proposed technique is able to extract features at different levels through forming a pyramid of regions-of-interest. Such pyramid is formed according to the inherent hierarchy of portal image features which are often evident only at corresponding levels. A sequence of operating steps, including histogram manipulation, Hough transform, optimal Canny edge detection, and pyramid feature merging, are employed to accomplish the processing at different levels of the regions-of-interest. With such processing hierarchy, we are able to overcome the difficulties encountered by the existing global processing-based techniques and produce accurate field edges and sufficient and meaningful anatomical landmarks for the subsequent feature matching and error correction procedures.
Adaptive quantization with spatial constraints in subband video compression using wavelets

Coding of the high frequency subbands has been recognized as the key to the success of subband coding. However, the existing schemes are not very efficient in exploiting the spatial and spectral localization properties resulting from wavelet-based subband decomposition. We present a novel adaptive quantization scheme with spatial constraints. This scheme is capable of exploiting simultaneously both the spectrally and spatially localized characteristics of the high frequency subbands. A multi-modal Laplacian distribution is used to model the spectral distribution of a high frequency subband and a Gibbs random field is employed to model the spatial constraints. The modeling is incorporated into a non-iterative MAP estimation to yield the quantized subbands. This quantization scheme reduces significantly the activities in the high frequency bands while preserving the perceptually important structures. Such an adaptive quantization makes the entire subband video compression scheme amenable to low bit rate coding.
Fully-Parallel Stochastic Decoder for Rate Compatible Modulation

Rate compatible modulations (RCMs) are attractive for achieving seamless and blind rate adaptation under time varying channel. Although the decoding of RCM is inherently parallel, the highly complex processing nodes, and routing congestion have prohibited the implementation of fully-parallel decoders for high throughput. In this paper, we propose a new stochastic decoding algorithm for RCM to achieve desired parallel decoding. This algorithm provides even much better decoding performance than the floating-point belief propagation decoding algorithm with 20 iterations. To evaluate the effectiveness of the proposed algorithm, we apply it for the implementation of a field-programmable gate-array fully-parallel stochastic decoder that decodes a  $ {400 \times 400}$  mapping matrix. Several novel structure techniques, including the RAM-based channel stream generator and the configurable variable node, are exploited to reduce the logical resources consumption. The implemented decoder achieves a clock frequency of 220 MHz and provides a maximum throughput about 136Mb/s at SNR = 10 dB and the number of symbols is 400. To the best of our knowledge, this decoder is the first reported fully-parallel RCM decoder which achieves the highest decoding throughput. This research validates the potential of stochastic RCM decoding as a practical approach for area-efficient and high throughput decoders.
Optimal resource allocation in energy harvesting-powered body sensor networks

This paper considers Body Sensor Networks (BSNs) with energy harvesting and develops efficient resource allocation strategies for such networks. In order to provide a sustainable, high-quality service as well as dealing with the heterogeneity problem of BSNs, an optimal transmission power and time slots allocation strategy is formulated and solved using Markov Decision Process (MDP) method. The long term system performance is also analyzed based on the energy harvesting model and packet arrival model. Simulation results demonstrate the effectiveness of the proposed strategy to provide sustainable, high-quality and high availability service.
Multiview Video transcoding: From multiple views to single view

As multiview video is gaining more and more attentions, Multiview Video Coding (MVC) standard has been under development by the Joint Video Team as an extension to H.264/AVC. There will be increasingly more multiview video sources for both high end and low end consumers. Since a variety of end user devices shall not have multiview video decoders installed, multiview video transcoder is therefore needed in order for these users to enjoy the videos encoded by MVC standard. In this paper, we propose an algorithm for transcoding any viewpoint in bitstreams encoded by MVC to bitstreams that can be decoded by H.264/AVC decoders, regardless of the MVC prediction structure. The key component of this algorithm is the design of motion information reuse to implement simple motion refinement for inter-view coded macroblocks. Such reduced complexity implementation is especially applicable to the low power mobile devices. Experimental results show that, comparing with cascaded algorithm, the proposed scheme, with much reduced complexity, only suffers moderate performance loss.
Privacy-preserving outsourcing of image global feature detection

The amount and availability of user-contributed image data have been dramatically increased during the past ten years. Popular multimedia social networks, e.g. Flicker, commonly utilize user image data to construct user behavior models, social preferences, etc., for the purpose of effective advertisement, better user retention and attraction, and many others. Existing practices of data utilization, however, seriously deteriorate users' personal privacy and have led to increasing criticisms and legislation pressures. In this paper, we aim to construct a privacy-preserving feature detection scheme over encrypted image data. The proposed system enables an interested party to perform a variety of image feature detection tasks, including visual descriptors in MPEG-7 standard, while protecting user privacy relating to image contents. We implement a prototype system based on somewhat homomorphic encryption scheme and the benchmark Caltech256 database. The experimental results show that our system can guarantee effective image feature detection without sacrificing user privacy.
Medium Access Control for Wireless Body Area Networks with QoS Provisioning and Energy Efficient Design

With the promising applications in e-Health and entertainment services, wireless body area network (WBAN) has attracted significant interest. One critical challenge for WBAN is to track and maintain the quality of service (QoS), e.g., delivery probability and latency, under the dynamic environment dictated by human mobility. Another important issue is to ensure the energy efficiency within such a resource-constrained network. In this paper, a new medium access control (MAC) protocol is proposed to tackle these two important challenges. We adopt a TDMA-based protocol and dynamically adjust the transmission order and transmission duration of the nodes based on channel status and application context of WBAN. The slot allocation is optimized by minimizing energy consumption of the nodes, subject to the delivery probability and throughput constraints. Moreover, we design a new synchronization scheme to reduce the synchronization overhead. Through developing an analytical model, we analyze how the protocol can adapt to different latency requirements in the healthcare monitoring service. Simulations results show that the proposed protocol outperforms CA-MAC and IEEE 802.15.6 MAC in terms of QoS and energy efficiency under extensive conditions. It also demonstrates more effective performance in highly heterogeneous WBAN.
Joint source and channel optimized block TCQ with layered transmission and RCPC

We present a fixed length block trellis coded quantization (TCQ) image coding method with layered transmission and rate compatible punctured convolutional codes (RCPC) for noisy channel image communication. Employing optimal bit allocation and TCQ, this fixed length coding scheme achieves a good compression ratio while avoiding the loss of synchronization between encoder and decoder in a noisy environment. The loss of synchronization, caused often by entropy coding in the presence of channel noise leads to catastrophic decoding errors and degrades the coding performance substantially. With layered transmission and RCPC protection, the experiments show that the PSNR performance degrades gradually compared with the schemes layered operation as the channel bit error rate increases.
Distributed Source Coding Under Noisy Transmission Environments

Distributed source coding (DSC) has been attracting more and more research interests recently, particularly for mobile wireless applications. However, most existing DSC schemes assume that the coded information is transmitted error free to the decoder end. In this paper, we report our latest investigations on distributed source coding under noisy transmission environments. It is expected that these investigations provide useful guidelines for practical DSC system design when the transmission is actually not error free. Especially, many current schemes in DSC adopted the channel coding principles to accomplish the source coding tasks. We shall illustrate that the channel coding-based DSC schemes possess some inherent noise combating capability to recover possible channel errors under noisy transmission environments. Specifically, we shall report the investigations of Turbo DSC under various environments: error free, noisy transmission with additional forward error correction (FEC) and noisy transmission without FEC. Our experimental results show that, with the same number of bits transmitted through noise channel, the unprotected Turbo DSC outperforms the FEC protected Turbo DSC.
Artifact reduction in low bit rate DCT-based image compression

This correspondence presents a scheme for artifact reduction of low bit rate discrete-cosine-transform-compressed (DCT-compressed) images. First, the DC coefficients are calibrated using gradient continuity constraints. Then, an improved Huber-Markov-random-field-based (HMRF-based) smoothing is applied. The constrained optimization is implemented by the iterative conditional mode (ICM). Final reconstructions of typical images with improvements in both visual quality and peak signal-to-noise ratio (PSNR) are also shown.
Joint Source-Channel-Authentication Resource Allocation for Multimedia overWireless Networks

In our previous work (Li et al., 2006), we have presented unequal authenticity protection (UAP), the methodology of effective protecting multimedia stream transmitted over error-prone wireless networks. In this paper, we extend the previous analysis and consider integrating UAP into the joint source-channel coding (JSCC) framework to achieve optimization of end-to-end quality of media content. We further illustrate the effectiveness of this system using an implementation on progressive JPEG coder.
Synthesis of directional texture based on multiresolution block sampling and constrained block movement

The synthesis of directional texture is particularly challenging. We present a novel directional texture method based on our previously proposed multiresolution block sampling (MBS) and constrained block movement. First, we estimate the dominant direction of a given directional texture. Using a modified random movement-control set, we incorporate an additional directional constraint into our basic method to produce a new directional texture. A number of directional textures are used to show the effectiveness of this extended method.
Streaming Mobile Cloud Gaming Video Over TCP With Adaptive Source–FEC Coding

Cloud gaming has emerged as a promising application to enable high-end game playing with thin clients. Transmission control protocol (TCP) is pervasively adopted as the transport-layer protocol in the mainstream cloud gaming systems for video communication. However, streaming mobile cloud gaming video using the TCP is challenged with several key technical barriers: (1) the performance limitations of wireless networks in bandwidth and reliability; (2) the high throughput demand and stringent delay constraint imposed by high-quality gaming video transmission; and (3) the deadline violations and throughput fluctuations caused by the packet retransmission and congestion control mechanisms in the TCP. To address these critical problems, this paper proposes an application-layer source-forward error correction (FEC) coding framework dubbed adaptive source-FEC coding over TCP (ESCOT). First, we analytically formulate the optimization problem of joint source-FEC coding to minimize the end-to-end distortion of real-time video communication over TCP. Second, we develop a heuristic solution for effective loss rate approximation, source rate control, and FEC coding adaptation. ESCOT is distinct from existing source-FEC coding schemes in proactively analyzing and leveraging the TCP characteristics. The proposed solution is able to effectively mitigate both consecutive and sporadic video frame drops caused by congestion and random packet losses. We conduct the performance evaluation through extensive emulations in the Exata platform using real-time gaming video encoded by the H.264 codec. Experimental results show that the ESCOT advances the state of the art with noticeable improvements in video peak signal-to-noise ratio, end-to-end delay, goodput, and frame success rate.
Towards maximal decodable rate for multi-rate multicast of digital media with network coding

Multi-rate multicasting has become more and more attractive in contemporary multimedia applications because of its efficiency in serving heterogeneous receivers with different rates commensurate with their capabilities. When network coding is applied to multi-rate media multicasting to achieve additional gain, we encounter several significant challenges in designing a scheme that attains maximum throughput for all heterogeneous users. In this paper we focus on the problem of designing optimal network coding based approach to maximize total received utility for multi-rate multicasting media encoded in a layered structure. Such a layered structure facilitates multi-rate media delivery that matches users' reception capabilities but creates certain undesired dependency between different layers. We propose a request-assign mechanism to enable sufficient information propagation between source node and receiver node before multicasting the media content. With the help of request messages, the proposed scheme can overcome challenges of the layered dependency and is able to determine maximal decodable layer for each individual receiver. Furthermore, the proposed scheme achieves maximal decodable rate with polynomial complexity. Experiment results on JPEG-2000 images verify the proposed scheme.
MPEG CDVS Feature Trajectories for Action Recognition in Videos

Visual Action Recognition on mobile phones is a challenging problem. Mobile and wearable devices deal with power, memory, computational and hardware constraints, which mandate robust and lightweight algorithmic implementations for sophisticated vision applications, like action recognition. Compact Descriptors for Visual Search (CDVS) is an MPEG7 standard for an accelerated visual search on mobiles. In our work, we propose a mobile action recognition framework which classifies actions by tracking CDVS feature trajectories of human subjects. The proposed method capitalizes on the sparse, salient and memory efficient properties of CDVS features. Although our recognition accuracies on standard action datasets KTH, UCF50, and HMDB is not superior to the CNN based methods, our work explores and proves the feasibility of using CDVS features for action recognition.
Video adaptation proxy for wireless Dynamic Adaptive Streaming over HTTP

This research studies Dynamic Adaptive Streaming over HTTP (DASH) in cellular broadband access networks (e.g. HSDPA, HSPA+, LTE). We propose a wireless DASH (WiDASH) proxy to enhance the Quality-of-Experience (QoE) of wireless DASH. WiDASH proxy locates at the edge between Internet and wireless core networks. Different from conventional DASH, whose rate adaptation logics are either implemented locally in the user equipment or in the DASH server, WiDASH proxy is in charge of video adaptation, which makes it feasible to perform global optimization over multiple concurrent DASH flows. WiDASH proxy improves DASH QoE by three novel technologies. The first one is architecture level innovation. WiDASH simultaneously explores the potential of both split TCP and parallel TCP. It splits the original TCP connection from DASH server to a wireless user into one wired TCP and multiple wireless TCPs. This approach is able to increase video streams' average throughput without sacrificing inter user fairness. Second, WiDASH proxy employs video quality aware dynamic prioritization. Low bit rate video streams are entitled with high priority to guarantee minimum QoE for users with bad wireless channel quality. A video stream's priority is gradually dropped to normal state with the growth of its bit rate so that inter flow fairness between video streams and background best effort flows are still maintained. Third, we design a multiple-input multiple-output adaptive optimal controller, based on adaptive control theory. This rate controller minimizes a quadratic cost function which is defined as the weighted sum of multiple concurrent DASH videos' distortion, bit rate variation, and playback jitter. The solution of the optimization problem implicitly coordinates multiple DASH streams to improve overall streaming QoE. Simulation results verify that WiDASH proxy is able to improve QoE by providing smoother video streams with higher average visual quality for wireless HTTP video streaming.
Multiple hierarchical image transmission over wireless channels

In this paper, we present a multiple hierarchical coding and transmission scheme in which both the fragile structure of set partitioning in hierarchical trees (SPIHT) source coding and the unequal importance of bits in the coded bitstream have been taken into account. Multiple subsampling is applied to split the wavelet coeffcients of the original image source into multiple subsources so that each subsource is a crude representation of the original image. The sequential dependence of coded bitstream is broken. As a result, the error propagation is limited to a single subsource which is also coded with SPIHT to achieve desired coding performance. Cyclic redundancy coder/rate compatible punctured convolutional coder (CRC/RCPC) channel coding has been used to offer unequal error protection (UEP) to the multiple coded bitstreams. In each substream all the bits in the same bit layer are protected with the same channel code. The higher the bit-layer, the more the channel coding protects. Experimented results show that this new scheme has a good error resilient performance over wireless channels with time varying and bursty errors characteristics. In particular the reconstructed images often demonstrate good visual quality.
JSM-2 Based Joint ECG Compression Exploiting Temporal and Structural Dependency

In Wireless Body Area Networks (WBAN), the electrocardiogram (ECG) signal is an important class of bio-signals which needs to be transmitted and stored for diseases diagnostics. Due to the resource limitation in WBAN, the large amount of ECG signals need to be compressed before transmission and reconstructed with high accuracy. In this paper, we propose a novel CS-based ECG compression scheme, which considers both structural dependency and temporal dependency among ECG signals. The received ECG heartbeats are first classified into different classes and the statistical support information (SSI) is then established for each class. By using the corresponding SSI, a more accurate partially known support (PKS) will be obtained and the joint reconstruction performance of ECG signals could be improved consequently. Simulation results show that the proposed ECG compression scheme outperforms existing schemes, especially when the dimension of sampled measurements is low.
Error resilient transcoding of Scalable Video bitstreams

We propose in this paper a novel error resilient transcoding scheme that can be placed at the boundary between wired and wireless networks via heterogeneous network links. This error resilient transcoder shall seamlessly complement the standard scalable video coding (SVC) bitstream to offer additional error resilient adaptation capability for receiving devices. The novel error resilient transcoding scheme consists of three different modules; each is designed to meet various levels of complexity need. The three modules are all based on the loss-aware rate-distortion optimization (LA-RDO) mode decision algorithm we have previously developed for SVC. However, each individual module can be tailored to different complexity requirements depending on whether and how the LA-RDO mode decision is implemented. Another innovation of this approach is the design of a fast rate control algorithm in order to maintain consistent bitrates between input and output of the transcoder. This rate control algorithm only needs picture-level bit information for training target quantization parameters. Simulation results demonstrate that, comparing with standard SVC, the proposed approach is able to achieve up to 4 dB gain for the enhancement layer video and up to 1 dB gain for the base layer video.
Distributed coding techniques for onboard lossless compression of multispectral images

In this paper, we propose a low complexity lossless compression scheme for multispectral images based on distributed coding. Data decorrelation operations are moved to decoder on the ground in order to design a lightweight yet very efficient encoder suitable for onboard applications. The decoder with abundant resources will perform spectral-spatial adaptive fuzzy prediction to generate high quality side information for distributed coding by capturing the spatially varying spectral correlation. The LDPC decoding is integrated with Markov random field (MRF) modeling aiming at joint exploitation of spectral correlation and spatial correlation. Simulations have been carried out to demonstrate that the proposed scheme is able to provide competitive performance with respect to the state-of-the-art 3D DPCM technique but with significantly lower encoding complexity.
High frame rate screen video coding for screen sharing applications

In this paper, we propose a high frame rate screen video compression scheme aiming at improving the interactive user experience on screen sharing applications. The proposed screen video compression is performed as two-layer coding: a base layer coding using the conventional video codec and an enhancement layer coding using the proposed open-loop coding scheme. For efficient frame level layer selection and compression, the content update of each frame is evaluated through global motion detection. The screen frame with significant content update is fed to the conventional video encoder in base layer. In contrast, the frame with little update is compressed in enhancement layer in which the duplicate content is indicated by global motion vector and skip flag while the updated content is encoded by distinct intra modes in terms of inherent local features. The experimental results demonstrate that for the screen video containing interaction, the proposed coding scheme can achieve 3.09ms/frame encoding rate and 2.33ms/frame decoding rate with efficient rate distortion performance.