nils-herrmann/clean_scirepeval huggingface dataset:
Event-Based Semantic Image Adaptation for User-Centric Mobile Display Devices

This paper proposes a semantic image adaptation scheme for heterogeneous mobile display devices. This scheme aims to provide mobile users with the most desired image content by integrating the content semantic importance with user preferences under limited mobile display constraints. The main contributions of the proposed scheme are: 1) seamless integration of mobile user supplied query information with low level image features to identify semantically important image contents; 2) integration of semantic importance and user feedback to dynamically update mobile user preferences; and 3) perceptually optimized adaptation for image display on mobile devices. In order to bridge the semantic gap for adaptation, we design a Bayesian fusion approach to properly integrate low level features with high level semantics. To accommodate the variation of user preferences, the system involves mobile users in the adaptation process with only a few simple feedbacks so as to present to the users most interesting content on mobile devices. Eventually, perceptually optimized adaptation is performed to present the best image content for mobile users according to mobile display capacities. Extensive experiments have been carried out based on several common events defined in Kodak's consumer image database. These experiments show that by utilizing the proposed semantic adaptation scheme with integration of the semantics and mobile user preferences, perceptually relevant adaptation can be effectively carried out to tailor the image towards user intentions under the mobile environment constraints.
Performance improvement in DTNs by packet size optimization

In space communication environments, most studies in delay tolerant networks (DTNs) adopt experimental approaches and they have shown that the utilization efficiency of link bandwidth can be affected by changing packet sizes at the bundle layer and the convergence layer. However, the lack of theoretical work makes it difficult to find the optimal packet sizes in general scenarios. In this paper, we focus on this important issue by performing a theoretical analysis to solve the packet size optimization problems in both one-hop and multihop DTNs. As our analysis is based on the widely used DTN architecture with Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), the theoretical results can be applied to any DTN scenario using this architecture. We formulate packet size optimization problems in DTNs as nonconvex optimization problems. Furthermore, a goodput enhancement algorithm (GEA) is also proposed to find the optimal packet sizes in order to maximize goodput in DTNs. We conduct numerical analysis as well as simulations to validate our theoretical analysis and study the factors that determine the optimal packet sizes for BP and LTP. Numerical results show that GEA achieves significant performance improvement compared with schemes where packet size optimization on BP and LTP is not jointly considered.
Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in Cellular Networks

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies.
Prediction-based dynamic relay transmission scheme for Wireless Body Area Networks

To support long-term pervasive healthcare services, communications in Wireless Body Area Networks (WBANs) need to be both reliable and energy-efficient. As a cooperative transmission method, relay transmission scheme works effectively in resisting shadowing effect and improving reliability in WBANs. However, the extra energy consumption introduced by relay transmission is very high, which can shorten the lifetime of the whole network. In this paper, temporal and spatial correlation models for on-body channels are first presented to better characterize the slow fading effect of on-body channels. Then a prediction-based dynamic relay transmission (PDRT) scheme that makes full use of the correlation characteristics of on-body channels is proposed. In the PDRT scheme, “when to relay” and “who to relay” are decided in an optimal way based on the last known channel states. Moreover, neither extra signaling procedure nor dedicated channel sensing period is needed. Simulation results show that the PDRT scheme achieves significant performance improvement in energy efficiency, as well as ensuring the transmission reliability.
Dynamic Hybrid Multi Routing Protocol For Ad Hoc Wireless Network

Dynamic hybrid multi routing protocol (DHMRP) is proposed to overcome re-discovered route path to be reply path in traditional routing protocol. The protocol utilizes the reply path based on the hybrid clustering hierarchical establishment of multi routing path to gain an automatic monitoring and repairing broken links in ad hoc networks. And due to reply path and multi routing path shall exist separately in network to gain mitigation traffic "bottlenecks" of ClusterHeads so that improving clusters stability. Performance comparison of DHMRP with AOMDV using Glomosim simulation shows that DHMRP is able to achieve a lower data delay and route discovery ratio and higher packets deliver ratio
Joint source and channel decoding with symbol re-alignment

The maximum a-posteriori probability (MAP) decoding of Huffman variable-length codes (VLCs) has been widely used in the joint source and channel decoding. This method exploits both source memory information and channel status information, but does not consider the symbol re-alignment as the conventional Huffman decoding does. In this letter, we show that self-synchronization strings (SSSs) of VLCs can be readily found and used with the MAP decoding. As a result, the performance of symbol error rate based on Levenshtein distance can be improved, with reduced decoding complexity.
Realizing Low-Cost Flash Memory Based Video Caching in Content Delivery Systems

To implement caching devices in content delivery systems, flash memory is preferable to hard disk drives from the performance perspective. Nevertheless, the higher bit cost of flash memory is one major obstacle for the wide real-life deployment of flash-based video caching. This paper presents a set of design solutions to address this cost issue. First, we present a flash memory error tolerance design strategy customized for video data storage, which can enable the use of lower-cost less-reliable flash memory chips for video storage. The cost challenge can also be addressed by reducing the video storage footprint through on-the-fly transcoding. However, direct transcoding suffers from a high implementation cost. We propose two design techniques that can largely reduce the transcoding complexity at minimal storage overhead in flash memory. All the developed design solutions share the common feature of cohesively exploring the characteristics of video coding and flash memory device physics. Their effectiveness has been well demonstrated through experiments with 20-nm MLC NAND flash memory chips and extensive simulations with representative video sequences.
MixCast modulation for layered video multicast over WLANs

The major challenge in wireless multicast is the heterogeneous channel conditions of multiple users. In video multicast, the combination of a layered video coding scheme and a layered transmission scheme can gracefully accommodate user heterogeneity. This paper presents MixCast, a novel physical layer scheme for layered transmission. The key innovation in MixCast is the rateless Euclidean symbol mapping which mixes base layer and enhancement layer bits into arbitrary number of wireless symbols using arithmetic weighted sum operation. This design brings two benefits when compared with the state-of-the-art physical layer technique known as hierarchical modulation (HM). First, MixCast uses a fixed modulation constellation, avoiding the complexity in adaptive modulation and coding when channel condition varies. Second, the rateless symbol mapping allows MixCast to achieve much smoother rates than the stair-shaped rates in HM. We implemented MixCast for typical video multicast over OFDM physical layer, and evaluate its performance against HM through both simulations and software radio testbed. In simulations, MixCast shows consistent gain of 3dB to 5dB over HM under various rate combinations. In the testbed experiments, MixCast achieves significant gain up to 15dB in video PSNR over HM for football sequence.
QoS Guaranteed Scalable Video Transmission Over MIMO Systems with Time-Varying Channel Capacity

In this paper, we present a novel QoS guaranteed scalable video transmission scheme over multi-input multi-output (MIMO) wireless systems. The proposed scheme is able to not only guarantee the QoS (required BER for base and enhancement layer in compressed video) and but also maximize the system throughput over time-varying MIMO channel. We assume that the estimated channel state information (CSI) at the receiver is available at the transmitter through feedback. In this scenario, under a transmit power constraint, optimal power allocation can be designed to satisfy QoS-guaranteed maximum throughput by a combination of several adaptive operations. These operations include channel adaptive sub-channel selection, power allocation based on water-filling (WF), and data rate maximizing power reallocation. The proposed power reallocation scheme enables us to obtain surplus power after selecting proper adaptive mode and reallocate to other subchannels so as to maximize the data rate. We present in this paper some detailed analysis as well as simulation results to demonstrate that QoS guaranteed video transmission over MIMO wireless systems can indeed be achieved based on scalable video coding and a sequence of adaptive operations.
Model based estimation of left ventricle motion

A model based estimation algorithm is presented for the analysis of left ventricle motion and deformation over a cardiac cycle. This model based approach allows the authors to decouple a nonlinear and complex estimation problem into simple and well structured sub-problems. They identify the global motion as the relative position and orientation change of the left ventricle as a whole and model its shape as a tapered ellipsoid. A recursive algorithm is developed by incorporating the modeling primitive into the estimation procedure. This recursive algorithm produces a good estimate of the global motion and left ventricle shape even when the given bifurcation points are unevenly distributed. Upon compensation for the global motion, a tensor analysis based approach is introduced to parameterize the localized deformation of left ventricle surface.<<ETX>>
Forepressure Transmission Control for Wireless Video Sensor Networks

Multi-source data transmission in wireless video sensor networks is a challenging problem because of the high bandwidth demand of video streams and the many-to-one traffic pattern. This paper proposes forepressure transmission control for efficient and fair video transmission in this scenario. Contrary to traditional backpressure transmission control where downstream node creates backpressure and causes upstream node to hold off when its buffer is full, forepressure transmission control creates forepressure and signals upstream node to transmit when the downstream node's buffer is not full. Forepressure transmission control proactively avoids congestion at little communication overhead. In addition, it is able to provide fairness among concurrent flows. We evaluate forepressure transmission control via NS-2 simulations, and compare it with both backpressure congestion control and end-to-end rate regulation. Results in three typical topologies show that forepressure transmission control achieves much lower loss ratio and ensures better fairness than the other two schemes.
Hybrid DCT-Wiener-based interpolation via improved spatial filter

Resizing video frames is one of the key techniques for multimedia processing. In particular, it is more desirable to generate high-resolution video frames from low-resolution sources to meet the needs of customers with larger screen displays and higher display requirement. The original hybrid DCT-Wiener-based interpolation scheme provides a powerful framework to interpolate video frames by combining the information in both spatial and DCT domain seamlessly. It can provide both better objective and visual qualities with low complexity than existing interpolation methods. This paper presents an enhancement over the original hybrid scheme to further improve its performance. We first analyze the bottleneck of the original hybrid interpolation scheme, and then propose a novel spatial interpolation scheme to further improve its performance. The spatial interpolation scheme designed in this research is a contour-enhanced Wiener filter with unsharp masking post-processing. It is learnt using training video frame pairs through the minimum mean square error estimation, which is able to resolve the quarter-pixel shift issue and achieve much better performance over the original 1-D 6-tap pixel-based Wiener filter. Experiments have been carried out to demonstrate that the proposed scheme achieves noticeable gains over the original hybrid interpolation scheme as well as other existing schemes in both objective and visual qualities.
Three dimensional subband video analysis and synthesis with adaptive clustering in high frequency subbands

This paper presents a hybrid analysis and synthesis scheme for video coding aimed at developing a video communication system that can consistently transmit high quality video signals with high compression ratios. The scheme is based on a 3D subband decomposition of video signal. To best take advantage of the characteristics of high frequency subbands, an adaptive clustering algorithm with Gibbs random field model is applied to the high frequency subbands to achieve an adaptive quantization with spatial constraints. The adaptive quantization reduces significantly the activity of high frequency bands and makes the entire scheme amenable to efficient video coding. The resultant reconstruction noise can be suppressed using again a Gibbs random field based algorithm. Preliminary results have shown the potential of this methods over existing methods.<<ETX>>
Layered compression for high-precision depth data

With the development of depth data acquisition technologies, access to high-precision depth with more than 8-b depths has become much easier and determining how to efficiently represent and compress high-precision depth is essential for practical depth storage and transmission systems. In this paper, we propose a layered high-precision depth compression framework based on an 8-b image/video encoder to achieve efficient compression with low complexity. Within this framework, considering the characteristics of the high-precision depth, a depth map is partitioned into two layers: 1) the most significant bits (MSBs) layer and 2) the least significant bits (LSBs) layer. The MSBs layer provides rough depth value distribution, while the LSBs layer records the details of the depth value variation. For the MSBs layer, an error-controllable pixel domain encoding scheme is proposed to exploit the data correlation of the general depth information with sharp edges and to guarantee the data format of LSBs layer is 8 b after taking the quantization error from MSBs layer. For the LSBs layer, standard 8-b image/video codec is leveraged to perform the compression. The experimental results demonstrate that the proposed coding scheme can achieve real-time depth compression with satisfactory reconstruction quality. Moreover, the compressed depth data generated from this scheme can achieve better performance in view synthesis and gesture recognition applications compared with the conventional coding schemes because of the error control algorithm.
Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications

Image segmentation remains one of the major challenges in image analysis. In medical applications, skilled operators are usually employed to extract the desired regions that may be anatomically separate but statistically indistinguishable. Such manual processing is subject to operator errors and biases, is extremely time consuming, and has poor reproducibility. We propose a robust algorithm for the segmentation of three-dimensional (3-D) image data based on a novel combination of adaptive K-mean clustering and knowledge-based morphological operations. The proposed adaptive K-mean clustering algorithm is capable of segmenting the regions of smoothly varying intensity distributions. Spatial constraints are incorporated in the clustering algorithm through the modeling of the regions by Gibbs random fields. Knowledge-based morphological operations are then applied to the segmented regions to identify the desired regions according to the a priori anatomical knowledge of the region-of-interest. This proposed technique has been successfully applied to a sequence of cardiac CT volumetric images to generate the volumes of left ventricle chambers at 16 consecutive temporal frames. Our final segmentation results compare favorably with the results obtained using manual outlining. Extensions of this approach to other applications can be readily made when a priori knowledge of a given object is available.
Test-pattern-reduced decoding for turbo product codes with multi-error-correcting eBCH codes

We present a method to reduce the number of test patterns (TPs) decoded in the Chase-II algorithm for turbo product codes (TPCs) constructed with multi-error-correcting extended Bose-Chaudhuri-Hocquengem (eBCH) codes. We classify TPs into different conditions based on the relationship between syndromes and the number of errors so that TPs with the same codeword are not decoded except the one with the least number of errors. For eBCH with code length of 64, simulation results show that over 50% of TPs need not to be decoded without any performance degradation.
Constrained SOVA decoding in concatenated codes

In practical communication systems, error detection and correction codes such as cyclic redundancy check (CRC) codes and turbo codes (TCs) are almost always concatenated. While TCs are used to remove errors, CRC codes may be applied to applications such as ARQ, early stop of turbo decoding. Considering the scenario that a large data frame for TC consists of multiple short packets, each with CRC parity bits, we propose a constrained-SOVA (C-SOVA) algorithm to fully take advantage of the interim CRC detection results to redesign the TC decoding algorithm. One more interleaver is added at the encoder so that bits in a correctly decoded packet are permuted to the entire range of frame. At the decoder, the C-SOVA has been designed that includes constraining trellis, updating the extrinsic information, and reducing the number of tracing back. As a result, not only a the decoding complexity is significantly reduced, but a high coding gain has been preserved due to elimination of certain error patterns as well. Simulations have been presented to demonstrate the excellent features of the proposed scheme. Although SOVA algorithm and concatenated codes are considered in this paper, the proposed idea using constraints is applicable to any trellis-based decoding algorithms including maximum a posterior probability (MAP) algorithm, and to any scenarios that partial bits are known correct in the course of turbo decoding.
Distributed image coding based on integrated Markov random field modeling and LDPC decoding

We present in this paper a novel distributed image coding scheme by exploiting image spatial correlation via Markov random field modeling at the decoding end. This allows us to design a simple yet efficient encoder suitable for various energy efficient imaging sensor network applications. The novelty is the integration of LDPC decoding and Markov random field modeling in order to jointly exploit both inter-image and intra-image correlation. The current research aims at improving our previous work in which the Markov model was defined by a state transition probability matrix. In this research, we model the image via a Markov random field described by Gibbs distribution. Both analysis and simulations have been carried out to demonstrate that this Markov model-based approach is able to achieve significant gains over the schemes without Markov modeling. Furthermore, this new Gibbs-based Markov model is less sensitive to correlated noise. Our approach also outperforms a JPEG codec by up to 4 dB even if the interimage correlation is not very high.
A novel product coding and decoding scheme for wireless image transmission

This paper presents a novel generic channel coding scheme for image transmission over wireless channels. The product code is constructed using one classical convolutional code in one direction and one recursive systematic code (RSC) in the other direction. At the receiving end, the data are alternatively decoded along the two directions. In each iteration, any correctly decoded data in one direction will be used to constrain the decoding along the other direction. The constrained Viterbi decoding algorithm is designed in such a way that the survival paths are forced to pass through the positions where the bits have already been correctly decoded. With such a constraint, we achieve a reduction of candidate paths for the maximum likelihood (ML) decoding as well as a speed up of convergence. Experimental results show that the proposed scheme is able to outperform all existing schemes under some random and bursty error channel conditions.