nils-herrmann/clean_scirepeval huggingface dataset:
CrowdDBS: A Crowdsourced Brightness Scaling Optimization for Display Energy Reduction in Mobile Video

Mobile display has become one of the most power-hungry components in mobile video viewing. Currently, mobile devices can reduce the display energy by performing dynamic brightness scaling (DBS) under the distortion constraint of video signals. We observe that there is a pitfall preventing current practice from systematic display energy reduction. In particular, existing objective DBS schemes lack direct connection to the subjective human perception on DBS-enabled videos, which is the key to achieving human-centered energy-experience optimization. To overcome this pitfall, we present CrowdDBS, a crowdsourced display energy reduction framework for mobile video viewing. CrowdDBS is empowered by a set of crowdsourcing studies that uncover the relationship between human perception and DBS frequency, magnitude, and temporal consistency, respectively. Motivated by the insights obtained from these studies, CrowdDBS employs a suit of designs and a DBS optimization framework to optimize the energy-experience tradeoff in mobile video viewing. Comprehensive experimental results and user evaluations under a variety of practical settings show that CrowdDBS can achieve 37 percent device energy reduction on average while guaranteeing satisfactory user experience in mobile video viewing.
Socialized Mobile Photography: Learning to Photograph With Social Context via Mobile Devices

The popularity of mobile devices equipped with various cameras has revolutionized modern photography. People are able to take photos and share their experiences anytime and anywhere. However, taking a high quality photograph via mobile device remains a challenge for mobile users. In this paper we investigate a photography model to assist mobile users in capturing high quality photos by using both the rich context available from mobile devices and crowdsourced social media on the Web. The photography model is learned from community-contributed images on the Web, and dependent on user's social context. The context includes user's current geo-location, time (i.e., time of the day), and weather (e.g., clear, cloudy, foggy, etc.). Given a wide view of scene, our socialized mobile photography system is able to suggest the optimal view enclosure (composition) and appropriate camera parameters (aperture, ISO, and exposure time). Extensive experiments have been performed for eight well-known hot spot landmark locations where sufficient context tagged photos can be obtained. Through both objective and subjective evaluations, we show that the proposed socialized mobile photography system can indeed effectively suggest proper composition and camera parameters to help the user capture high quality photos.
Constrained decoding for turbo-CRC code with high spectral efficient modulation

In this paper, we propose a constrained decoding algorithm for turbo-CRC codes with high spectral efficient modulation. We follow a data structure adopted in 3GPP in which the large data frame used for turbo coding consists of multiple CRC packets. First, we modify the turbo encoder by introducing an additional interleaver to permute correctly decoded bits at early stages of decoding over the entire frame. Then, we propose a constrained decoding algorithm at the receiver, making use of the information of correctly decoded bits to help the decoding of other bits and reduce the complexity. Experimental results demonstrate the excellent performance of the entire system. In addition, the error localization property inherent in the system may benefit other applications such as ARQ.
Low complexity fair queueing driven wireless multiuser scheduler

Adaptive modulation and coding (AMC) and opportunistic scheduling are widely used in modern broadband wireless communication systems to adapt to time-dependent user-dependent channel fading. On one hand, AMC and opportunistic scheduling can adapt to channel variations and take advantage of multiuser diversity to effectively improve throughput. On the other hand, due to the user-dependent wireless channel conditions, opportunistic scheduling may be sacrifice short term throughput fairness among users and may introduce large packet delivery delay. Both may result in unacceptable quality-of-experience for delay sensitive traffic such as video teleconferencing. To overcome this drawback in opportunistic scheduling, we propose fair queueing driven multiuser scheduling (FQ-MS) for wireless communication systems. FQ-MS directly resorts to data structure, rather than iterative numerical optimization techniques to guarantee strict short term fairness and acceptable delay. It is indeed a synergy of fair queueing and opportunistic scheduling. Its fair queueing mode enforces deterministic short term fairness while its opportunistic scheduling mode explores stochastic multiuser diversity. FQ-MS is able to guarantee: 1) both short term and long term fairness; 2) high throughput; 3) computational efficiency. Theoretical analysis and simulation results are presented to verify that FQ-MS fulfills these desired features.
A Joint Source-Channel Adaptive Scheme for Wireless H.264/AVC Video Authentication

Authentication has become an emerging issue for video streaming over lossy networks. Although the advanced video coding standards, such as H.264/AVC, efficiently reduce the amount of data to be transmitted, the coding dependency brings new challenges in designing efficient stream authentication scheme. In this paper, we propose a novel joint-designed-layered source-channel adaptive scheme that integrates authentication into source and channel coding components to sufficiently use the related information to efficiently address the coding dependency and to design the optimal rate allocation scheme for the sake of end-to-end video quality. The proposed layered framework is able to minimize end-to-end quality degradation incurred by both the wireless channel noise and the authentication failure. In particular, the competing requirements of high verification probability and low authentication overhead are concurrently satisfied by the elegant design of layered hash appending with efficient adaptation to the H.264 source coding and channel conditions. A joint source-channel-authentication rate allocation scheme is then developed to achieve optimal end-to-end video quality. The experimental results on H.264 video sequences confirm the efficacy of this joint adaptive scheme and demonstrate that it indeed outperforms the state-of-the-art graph-based authentication algorithms.
An Efficient Decoder for Turbo Product Codes with Multi-Error Correcting Codes

In this paper, Chase decoding algorithm for turbo product codes (TPCs) built with multi-error-correcting codes is investigated. A method is proposed to reduce the decoding of test patterns (TPs) as well as the calculation of syndromes and metrics. In the beginning, syndromes and metrics of the first half of TPs are calculated recursively. After decoding of one TP, according to the error bit positions, other TPs that will have the same decoded codeword are identified and rejected from decoding. In the mean time, if these TPs fall into the range of second half of TPs, their syndromes and metrics need not to be calculated either. Simulations results for TPCs built with eBCH(64,51,6,2) or eBCH(64,45,8,3) are presented to demonstrate the efficiency of the proposed method. Moreover, this method that reduces the inherent redundancy in Chase decoding does not cause any degradation in coding gain.
Image transmission over noisy channels with variable-coefficient fixed-length coding scheme

A variable-coefficient fixed-length coding scheme is proposed for wavelet-based image transmission over noisy channels. Multiple subband coefficients are grouped into an extended source symbol and coded as a fixed-length code word such that the extended source symbols have almost the same probabilities. Part of the codebook is fixed based on the observation of the coefficient spatial distribution patterns in each subband to alleviate the transmission of the codebook. The remaining code positions within the fixed-length codebook can be utilized to combat channel errors by carefully arranging the code positions or be filled with other frequently appearing coefficient sequences to achieve higher compression ratio.
Assessing photo quality with geo-context and crowdsourced photos

Automatic photo quality assessment emerged as a hot topic in recent years for its potential in numerous applications. Most existing approaches to photo quality assessment have predominantly focused on image content itself, while ignoring various contexts such as the associated geo-location and timestamp. However, such a universal aesthetic assessment model may not work well with significantly different contexts, since the photography rules are always scene and context dependent. In real cases, professional photographers use different photography knowledge when shooting various scenes in different places. Motivated by this observation, we leverage the geo-context information associated with photos for visual quality assessment. Specifically, we propose in this paper a Scene-Dependent Aesthetic Model (SDAM) to assess photo quality, by jointly leveraging the geo-context and visual content. Geo-contextual leveraged searching is performed to obtain relevant images with similar content to discover the scene-dependent photography principles for accurate photo quality assessment. To overcome the problem that in many cases the number of the contextually searched images is insufficient for learning the SDAM, we adopt transfer learning to utilize auxiliary photos within the same scene category from other locations for learning photography rules. Extensive experiments shows that the proposed SDAM scheme indeed improves the photo quality assessment accuracy via leveraging photo geo-contexts, compared with traditional universal aesthetic models.
QoS-Driven Power Control for Inter-WBAN Interference Mitigation

Wireless Body Area Networks (WBANs) are usually designed for pervasive healthcare applications. Since the primary traffic in WBAN is vital physiological signals, guaranteeing the Quality of Service (QoS) is crucial while designing WBAN. However, QoS of WBAN will be degraded in strong inter-WBAN interference environment such as hospitals and senior communities, where WBANs are densely deployed. In this paper, by focusing on a more practical WBAN model, we propose a non- cooperative power control game to mitigate inter- WBAN interference, in which the cost function is well designed by considering both QoS requirement and energy constraint. The existence of at least one Nash equilibrium (NE) point for the game is proved and a sufficient condition for the uniqueness of the NE is derived. To guarantee non-cooperative among WBANs, an interference segmentation estimate (ISE) algorithm is proposed to obtain an approximation of the NE point. Simulation results demonstrate the effectiveness of the proposed ISE algorithm.
Test Compression with Single-Input Data Spreader and Multiple Test Sessions

Test time and test data volume required to test modern integrated circuits grow rapidly with circuit complexity. Test compression is now widely used in industry to reduce test cost. In this paper, a simple yet highly efficient test data compression technique with small area overhead is presented. Efficient algorithms are developed to determine configurations of the test decompressor and corresponding test patterns in multiple test sessions. These algorithms result in higher test compression ratio and lower test application time with less CPU runtime compared to the latest previous work. Experimental results on IWLS'05 benchmark circuits show that on average we can increase the compression factor by 17.26%, decrease the test application time by 12.28% and cut down the CPU time by 42.92%, with only slight increase of area overhead. More importantly, up to 1500x test compression factor is achieved for a design containing about 2M gates, with only 0.1% area overhead.
Seamless Video Transmission over Wireless LANs based on an Effective QoS Model and Channel State Estimation

This paper presents a scheme for video transmission over wireless LANs based on an effective QoS model and channel state estimation (CSE). The proposed QoS model is able to effectively characterize the performance of video transmission over error prone wireless channels. Unlike existing schemes that use complicated metrics based on the actual distortion computed from decoded video frames, the proposed model employs H.264 group of pictures (GOP) level estimator of decodable slice rate (GEDSR). This new GEDSR model can be obtained from simplified calculation of decodable I, P, and B slices without computing the actual distortions. With this model, it is possible for the estimation to include the dependence of error control such as forward error correction and redundant slices. In this research, we also develop an adaptive cross-layer feedback mechanism based on channel status, maximum retry limit value, and RTS/CTS function. Extensive simulations have been carried out to confirm the effectiveness of the proposed GEDSR model for the distortion analysis. The experimental results of the GEDSR model analysis have been applied to selecting forward error correction (FEC) coding parameters for video streams to respond to dynamic channel changes. Simulations on video transmission over wireless LAN show that the proposed scheme is able to achieve significant performance gains (7dB) over schemes without channel feedback.
Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering

We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor readings. In this paper, we further address two key problems in the CDG framework. First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account. Excitingly, we discover that a simple form of measurement matrix [I R] has good RIP, and the data gathering scheme that realizes this measurement matrix can further reduce the communication cost of CDG for both chain-type and tree-type topology. Second, although the sparsity of sensor readings is pervasive, it might be rather complicated to fully exploit it. Owing to the inherent flexibility of CS principle, the proposed CDG framework is able to utilize various sparsity patterns despite of a simple and unified data gathering process. In particular, we present approaches for adapting CS decoder to utilize cross-domain sparsity (e.g. temporal-frequency and spatial-frequency). We carry out simulation experiments over both synthesized and real sensor data. The results confirm that CDG can preserve sensor data fidelity at a reduced communication cost.
Stateful Scheduling with Network Coding for Roadside-to-Vehicle Communication

In urban areas such as street blocks, media-rich services are delivering to moving vehicles through Access Points (APs) deployed on the roadside. To increase the capacity of wireless links for these services, a roadside-to-vehicle communication system based on a novel Stateful Scheduling with Network Coding (SSNC) strategy is proposed. A major innovation of SSNC is that it enables the scheduling on the roadside AP to fully utilize the states of received data from vehicles for an enhanced performance. Furthermore, a set of mechanisms are proposed to ensure reliable transmission in such highly dynamic and error-prone wireless channels. Extensive simulations show that SSNC achieves an increased throughput gain between 1.2 and 2.0 at various conditions.
A novel volumetric feature extraction technique with applications to MR images

A semiautomated feature extraction algorithm is presented for the extraction and measurement of the hippocampus from volumetric magnetic resonance imaging (MRI) head scans. This algorithm makes use of elements of both deformable model and region growing techniques and allows incorporation of a priori operator knowledge of hippocampal location and shape. Experimental results indicate that the algorithm is able to estimate hippocampal volume and asymmetry with an accuracy which approaches that of laborious manual outlining techniques.
Assessing quality of experience for adaptive HTTP video streaming

In this paper, we present a novel subjective quality model for online adaptive movie streaming service. The proposed model considers the Quality of Experience (QoE) of streaming video viewing as a cumulative evaluation process of consecutive segments that compose a story line. Under bandwidth constraint, streaming client may select lower-quality segment, pause playback for re-buffering, or both. The momentary QoE loss at these events are weighted by the content suspension level. Meanwhile such experience penalty remains to influence user's opinion for the rest of the service. If the picture becomes too noisy or the interruptions occur consistently, the user may stop watching. The proposed scheme includes two parts. First, a parametric model estimates the quality loss of a interfered segment based on its network-level packet characteristics. Second, a cumulative function integrates the impact of streaming events. Both steps demand minimum computation and can be updated in real time. A subjective test to train and validate the proposed parametric model is designed and performed. This model is fundamentally different from all existing QoE assessment schemes in that temporally cumulative viewing experience of the users, instead of simple global statistics, is evaluated.
Wyner-Ziv video coding using progressive encoding and decoding

In distributed single-view video coding (DSVC) or distributed multiview video coding (DMVC), the compression performance is greatly affected by the quality of side information (SI). Most existing DSVC or DMVC schemes generate SI by employing motion estimation (ME) at the decoder. Since the original frame is unavailable, the ME accuracy is low. In this paper we propose a novel distributed coding scheme for both single-view video and multi-view video. At the encoder, each Wyner-Ziv frame (WF) is sub-sampled using a block-based sub-sampling algorithm, and resulting sub-samples are successively encoded and transmitted. For the latter samples, adaptive intra prediction is employed with no intra mode bits transmitted. At the decoder, the current WF is progressively decoded, and thus the ME accuracy is iteratively improved. Experimental results demonstrate that the proposed DSVC and DMVC framework has achieved significant rate distortion (RD) gain.
Reference Image Based Color Correction for Multi-camera Panoramic High Resolution Imaging

Maintaining color consistency across panoramic video captured by multi-camera array is a challenging problem. In an uncalibrated multi-camera array with automatic camera settings, each camera individually adjusts its parameters in accordance with the region of scene captured, independent of adjacent cameras. This leads to inconsistency in intensity and color in the stitched panoramic image. Selection of one of the images as reference for color correction may yield poor results because no individual camera image may represent the color palette of entire scene. We address these issues by capturing a separate low resolution reference color image with a field of view that encompasses entire scene. The color statistics of the reference image are used to bring each camera array image into a uniform radiance and color palette. We then estimate optimal color correction parameters using a joint pairwise optimization that minimizes overall error in stitched panorama, thus achieving a fast and robust color correction scheme for multi-camera panoramic high resolution imaging.
MMC02-6: Low Punctured Turbo Codes and Zero Motion Skip Encoding Strategy for Distributed Video Coding

In this paper, we present a distributed video coding scheme based on low punctured turbo codes and zero motion encoding strategy. Most recent distributed video coding schemes have a common structure: the encoded bit streams consist of both side information derived from previous frames and syndrome bits derived from channel coding such as Turbo codes. Turbo codes are often punctured to generate scalable compressed video bit streams. We demonstrate in this paper that the generation of side information and the design of the punctured Turbo codes can be integrated to improve the overall coding performance. In the encoding of the video frames, zero motion blocks can be identified and made known to decoder so that the reference frames at the decoder shall have some blocks remain unchanged. While constructing the frame at the decoder, the Turbo decoder may be able to use this information as a constraint to achieve an improved performance. However, we discover a better way of making use of zero motion blocks by skipping the encoding of these blocks. The skip at the encoder will result in the direct reduction of the video coding bit rate as well as lower punctured Turbo codes. Since low punctured Turbo codes perform better, the combined effects on the reduced rate side information and an improved Turbo coding performance leads to an overall performance improvement. Simulation results based on this approach show that integrated low punctured Turbo codes and zero motion skip encoding strategy indeed achieves better performance than the constrained punctured Turbo coding. Furthermore, this scheme can be extended to other channel coding schemes, such as Product Accumulate code, that have been adopted in distributed video coding.
Adaptive Initial Quantization Parameter Determination for H.264/AVC Video Transcoding

Video adaptation through transcoding can provide both bit-rate reduction and resolution reduction to meet various requirements from display devices to network links. One important issue in video transcoding is the design of rate control algorithm in order to achieve target bit rate by adjusting certain coding parameters. Among them, proper selection of initial quantization parameter (QP) has been shown to induce noticeable impact on the performance of video transcoding scheme. Current approaches in initial QP determination are either too complicated or lacking adequate accuracy. This paper presents an adaptive QP initialization for H.264/AVC transcoding. First, we carefully build the models of R-MSE and QP-PSNR. Then, we introduce an R-QP model and allocate an optimal target buffer to the first frame by considering its temporal importance. The analysis and the R-QP model lead to a novel scheme to determine the initial QP adaptively to achieve more accurate estimation. Experiments have been carried out to demonstrate that substantial gains in objective quality measures can be consistently obtained. Without increasing complexity in transcoding system, the proposed adaptive initial QP scheme outperforms current existing schemes for various video sequences tested in this research.