nils-herrmann/clean_scirepeval huggingface dataset:
Normal mesh compression based on rate-distortion optimization

An efficient progressive compression and interactive transmission algorithm for 3D normal meshes, based on rate-distortion optimization, are proposed in this work. A normal mesh is partitioned into several segments, which are encoded independently. By truncating the bitstream for each segment optimally using a distortion model, the proposed algorithm yields a higher coding gain than the conventional approach. Moreover, to support interactive transmission of 3D models from client's viewpoint, each segment can be endowed with different priority of transmission. The view-dependent processing technique enables to reduce the transmission bandwidth considerably while maintaining the same visual quality.
Adaptive joint source/channel coding for robust video transmission

A robust video transmission technique that protects video signals adaptively against channel errors is proposed in this work. First, we develop a source model, which estimates the bit rate and the quantization error. Second, the encoder associates each packet with the mean square error between the error-free reconstruction and the concealed reconstruction. Third, the quantization parameter and the channel code rate are adaptively assigned to each packet so that the expected mean square error due to source and channel distortions is minimized subject to a constraint on the total transmission bit rate. Simulation results show that the proposed algorithm provides acceptable image quality in a high bit error rate environment.
A feature-based approach to fast H.264 intra/inter mode decision

A feature-based fast intra/inter mode decision method is proposed to reduce the encoder complexity of the H.264 video coding standard in this work. The main idea is to decide the mode using the expected risk of choosing the wrong mode in a multidimensional feature space. The proposed algorithm calculates three features and maps them into the one of three regions; namely, risk-free, risk-tolerable, and risk-intolerable regions. Depending on the mapped region, we can apply algorithms of different complexities for the final mode decision. It is demonstrated by experimental results that the proposed algorithm can save approximately 20-32% of the total encoding time of H.264 (JM7.3a) with little degradation in the rate-distortion performance.
TAEF: A cross-distance/environment face recognition method

A solution to long distance outdoor face recognition is presented in this work. The proposed method, called the Two-Stage Alignment/Enhancement Filtering (TAEF) system, consists of three main components: a cross-distance face alignment technique, a cross-environment face enhancement technique, and a two-stage filtering system. Given a probe image, the procedure of face alignment, enhancement and matching is executed against all gallery images to eliminate unlikely candidates at once at the first stage for efficiency. Then, the procedure is conducted for every individual probe/gallery image pair for higher accuracy at the second stage. The first rank recognition rates of the TAEF method are 100%, 100% and 97% for 60-, 100- and 150-meter visible-light images in the LDHF database, respectively.
Scalable dynamic code assignment for OVSF-CDMA systems

Two scalable dynamic code assignment (DCA) schemes with call admission control for OVSF-CDMA systems are studied in this research. The proposed schemes generate an average data throughput of the system close to that of the optimal scheme while demanding much lower design and implementation complexity than the optimal scheme. The capacity-partitioning scheme partitions the whole resource into several mutually exclusive subsets of resource and assigns each subset of resource to a group of users in proportion to the corresponding traffic load. The class-partitioning scheme partitions the set of service classes into mutually exclusive groups of classes and assigns a subset of the total resource to the corresponding group of classes. In either case, the call requests that belong to each subset of resource or each group of classes are served independently of others by the optimal DCA. Numerical evaluation confirms the superior performance of the proposed schemes with low complexity.
A game theoretic approach to eavesdropper cooperation in MISO wireless networks

Information theoretic security, also called secrecy analysis, provides theoretical limit for secret data transmission even in wireless networking environment, and it is more focused in these days because of increasing number of private information transmission through wireless channel and its inherent broadcasting characteristic. However, many studies in secrecy analysis assume that there exists only one eavesdropper or there is no cooperation among multiple eavesdroppers, which makes research results difficult to be applied in real situations. In this paper, we conduct a game theoretic analysis of eavesdropper cooperation in MISO wireless communication system where a secret information sender, Alice, has a counter-strategy against eavesdropping. We assume that Alice has a method for preventing eavesdroppers from overhearing. It is triggered by eavesdropper detection and degrades eavesdropping channel by forwarding artificial noise. For this reason, eavesdroppers' cooperation can pose a high risk to them and we utilize game theory to investigate eavesdropper behavior based on mutual information I (X; Yeve) between the input X from Alice and the output Yeve to an eavesdropper. We derive that non-cooperation is Nash Equilibrium in one shot eavesdropper cooperation game and show necessary conditions for eavesdropper cooperation in infinitely repeated game.
Optimized opportunistic multicast scheduling (OMS) over wireless cellular networks

Optimized opportunistic multicast scheduling (OMS) is studied for cellular networks, where the problem of efficiently transmitting a common set of fountain-encoded data from a single base station to multiple users over quasi-static fading channels is examined. The proposed OMS scheme better balances the tradeoff between multiuser diversity and multicast gain by transmitting to a subset of users in each time slot using the maximal data rate that ensures successful decoding by these users. We first analyze the system delay in homogeneous networks by capitalizing on extreme value theory and derive the optimal selection ratio (i.e., the portion of users that are selected in each time slot) that minimizes the delay. Then, we extend results to heterogeneous networks where users are subject to different channel statistics. By partitioning users into multiple approximately homogeneous rings, we turn a heterogeneous network into a composite of smaller homogeneous networks and derive the optimal selection ratio for the heterogeneous network. Computer simulations confirm theoretical results and illustrate that the proposed OMS can achieve significant performance gains in both homogeneous and heterogeneous networks as compared with the conventional unicast and broadcast scheduling.
Adaptive search range selection in motion estimation

An adaptive search range (SR) selection algorithm in motion estimation for video encoding is proposed in this work with an objective to achieve excellent coding performance with reduced memory access bandwidth. In modern fast motion search algorithms, a motion vector predictor (MVP) is obtained from the motion vectors (MVs) of spatially and temporally neighboring blocks, which is called the predictive MV set. By exploiting the relationship between the variance of the predictive MV set and the SR, we develop an adaptive SR selection algorithm. That is, a larger variance implies lower accuracy of the MVP and, thus, a larger SR. The superior performance of the proposed SR selection algorithm is demonstrated by experimental results.
Cross-Layer Design for Wireless Video Streaming

We present a cross-layer approach to the design of a wireless video streaming system, which includes the development of an adaptive radio link buffer and congestion control algorithm and the adoption of the H.264/SVC scalable video format. The proposed system performs a group-of-pictures (GOP) based post-channel adaptation, which does not require the channel status information at the media server. Video packets are prioritized in a GOP-by-GOP manner based on the dependence relation within a GOP. We employ H.264/SVC for the video stream preparation to provide the flexibility and adaptability of compressed video data. The priority information is conveyed in the header extension of the network abstraction layer unit (NALU) of H.264/SVC, which is parsed by a media aware network element (MANE) to realize an efficient transmission and congestion control. The performance of the proposed algorithm is verified by the computer simulation.
CSMA/CA MAC Protocol Design for Topology Controlled Ad Hoc Networks: A Cross Layer Approach

Most topology control algorithms proposed so far are mainly for the power savings with the variable transmission powers taking no account of the effect from the underlying MAC protocol. When topology control is adopted in wireless ad-hoc networks with the fact that each node can vary its transmission power, there arise several issues, including throughput degradation, hidden node problem, and fairness problem, that affect the underlying MAC protocol design. In this paper, we investigate these issues when topology control is employed in wireless ad-hoc networks. We show that the throughput performance of MAC protocol is severely degraded when RTS-CTS is transmitted with maximum power. Also, the hidden node problem becomes very significant if RTS-CTS range is reduced improperly. We propose an algorithm to set up RTS-CTS range that can alleviate the hidden node problem, avoid the degradation of the throughput performance of MAC protocol, and resolve the fairness problem.
Contour line extraction with wireless sensor networks

An algorithm to extract contour lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, contour lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the contour line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.
Online bandwidth-efficient scheduling for video-on-demand with recursive patching

The service bandwidth optimization problem for multicast video-on-demand systems with early service merging is investigated, and a series of on-line algorithms based on recursive patching is proposed. We adopt a simple fixed-threshold starting rule and introduce a control window to regulate the degree of service merging. Our results indicate that cost-aware recursive patching (CARP) with a carefully chosen control window can significantly reduce the service bandwidth consumption. We also study the promoting rule and the limited client buffer effect for practical recursive patching algorithms. Experimental results show that the proposed recursive patching schemes outperform graceful patching by a wide margin, even with a very small client buffer size.
Novel Rate-Quantization Model-Based Rate Control With Adaptive Initialization for Spatial Scalable Video Coding

A novel spatial-layer rate-control algorithm is proposed for scalable video coding (SVC). First, by analyzing the relationship among the best initial quantization parameter (<i>Q</i><sub>p</sub>), channel bandwidth, and the initial frames' complexity measure, an adaptive <i>Q</i><sub>p</sub>-initialization model is introduced to determine the starting <i>Q</i><sub>p</sub> value for not only the base layer but also the spatial enhancement layers of SVC. Then, a two-stage <i>Q</i><sub>p</sub>-determination scheme is designed to improve the rate-control performance for the spatial-layer SVC with an efficient frame-complexity prediction method and an adaptive model-parameter update technique employed. Experimental results demonstrate the effectiveness of the proposed <i>Q</i><sub>p</sub> -initialization scheme and the two-stage <i>Q</i><sub>p</sub>-determination algorithm. By comparison with two other benchmark rate-control algorithms, the proposed rate-control algorithm is able to control constrained bit rates accurately with better rate-distortion performance.
Improved FOR/SOR-based video coding and its performance analysis

The FOR/SOR algorithm was initially proposed in  and shown to outperform H.264/AVC by a significant margin for high definition (HD) video coding when the bit rate is equal to 20Mbps or higher. In this work, we propose an improved FOR/SOR coding scheme which offers a superior coding performance in a bit rate range of practical interest, which ranges from 1–15 Mbps. To enhance the coding performance of the proposed algorithm, we incorporate several new coding tools in the basic FOR/SOR coding algorithm in . Furthermore, we attempt to give an intuitive explanation of the superior performance of the FOR/SOR coding scheme using arguments based on the R-D analysis. Finally, it is shown by experimental results that the proposed algorithm outperforms H.264/AVC significantly for HD video in the bit rate range of common interest.
Efficient dictionary based video coding with reduced side information

In this paper, we propose a novel dictionary based video coding technique with adaptive construction of overcomplete dictionaries and advanced coding methods tailored to sparse signal representations. A set of dictionaries is trained off-line using inter or intra predicted residual samples and is applied for encoding. New coding tools are developed so that the encoder can more compactly represent the residual signal. The same set of dictionary elements can be reused for neighboring blocks, and the optimal number of dictionary elements can be decided using rate-distortion optimization. Experimental results demonstrate that the proposed algorithm yields both improved coding performance and improved perceptual quality at low bit rates.
Coexistence Mechanism Using Dynamic Fragmentation for Interference Mitigation between Wi-Fi Bluetooth

In this paper, we present a non-collaborative coexistence mechanism using dynamic fragmentation that functions very well at the Wi-Fi in the presence of Bluetooth interference. The proposed scheme tries to optimize MAC layer packet length such that the Wi-Fi device has better chance to avoid the interference caused by Bluetooth devices. The mechanism dynamically adjusts the fragmentation level based on the current packet error rate (PER). We developed an analytical model that provides the MAC the necessary information (in terms of PER) to decide the right time for further packet fragmentation. The developed model is also employed to measure the throughput of the Wi-Fi device. Simulations are also performed to validate the developed model. We show that our coexistence mechanism could significantly improve the performance of Wi-Fi in both throughput and transmission delay, though there is only slight performance improvement at the Bluetooth side
An improved algorithm for fractal estimation from noisy measurements

The authors first show that when the increments of sampled fractional Brownian motion (fBm), also known as discrete fractional Gaussian noise (DFGN), are set equal to the finest scale wavelet approximation coefficients and when the Haar basis is selected, the discrete wavelet transform (DWT) coefficients are weakly correlated and have a variance that is exponentially related to scale. The observation motivates a new fractal estimation algorithm, which is a variant of an algorithm introduced by Wornell and Oppenheim ( March 1992) with the sampled fBm replaced by the increments of the sampled fBm. The performance of the new algorithm is compared with that of Wornell and Oppenheim's algorithm in numerical simulation results.<<ETX>>
Multidimensional humming transcription using a statistical approach for query by humming systems

A new statistical pattern recognition approach applied to human humming transcription is proposed. A musical note has two important attributes, i.e. pitch and duration. The proposed algorithm generates multidimensional humming transcriptions, which contain both pitch and duration information. Query by humming provides a natural means for content-based retrieval from music databases, and this research provides a robust frontend for such an application. The segment of a note in the humming waveform is modeled by a hidden Markov model (HMM), while the pitch of the note is modeled by a pitch model using a Gaussian mixture model. Preliminary real-time recognition experiments are carried out with models trained by data obtained from eight human subjects, and an overall correct recognition rate of around 80% is demonstrated.
Performance analysis of a class of multistage DS-CDMA receivers for multipath channels

Performance of the output signal-to-interference-plus-noise ratios (SINRs) and bit error rates (BERs) of the maximum ratio combining (MRC) multistage minimum-mean squared error (MMSE), minimum output energy (MOE), best linear unbiased estimator (BLUE) and maximum likelihood (ML) receivers are analyzed for direct-sequence code-division multiple access (DS-CDMA) systems in multipath Rayleigh fading channels, based on the common multistage structure we proposed previously (Sau-Hsuan Wu et al.,  2002, 2003;  2003). The SINRs of these receivers are proved to be monotonically increasing with the number of applied stages. The upper bound of output SINRs and its achievability conditions are also provided, which are shown to form the condition for the occurrence of BER floors of the multistage MRC MMSE/MOE/BLUE/ML multiuser receivers. A rule for selecting the number of stages is thus provided to take full advantage of the multistage structure of these receivers.
A deformable wavelet descriptor and its application to curve extraction from noisy images

We developed a hierarchical planar curve descriptor which decomposes a curve into components of different scales by using the wavelet transform in our previous work. We interpret the wavelet coefficients as random variables and call it the deformable wavelet descriptor. The application of the deformable wavelet descriptor to boundary curve extraction from noisy images is examined. Due to the multiscale and local support properties of the wavelet basis, the deformable wavelet approach performs better than the traditional Fourier approach. Experimental results are given to demonstrate the performance of the proposed method in extracting the boundary curve of an object from low SNR images.<<ETX>>
Compressed image quality metric based on perceptually weighted distortion

Objective quality assessment for compressed images is critical to various image compression systems that are essential in image delivery and storage. Although the mean squared error (MSE) is computationally simple, it may not be accurate to reflect the perceptual quality of compressed images, which is also affected dramatically by the characteristics of human visual system (HVS), such as masking effect. In this paper, an image quality metric (IQM) is proposed based on perceptually weighted distortion in terms of the MSE. To capture the characteristics of HVS, a randomness map is proposed to measure the masking effect and a preprocessing scheme is proposed to simulate the processing that occurs in the initial part of HVS. Since the masking effect highly depends on the structural randomness, the prediction error from neighborhood with a statistical model is used to measure the significance of masking. Meanwhile, the imperceptible signal with high frequency could be removed by preprocessing with low-pass filters. The relation is investigated between the distortions before and after masking effect, and a masking modulation model is proposed to simulate the masking effect after preprocessing. The performance of the proposed IQM is validated on six image databases with various compression distortions. The experimental results show that the proposed algorithm outperforms other benchmark IQMs.
A cross-Layer quality-of-service mapping architecture for video delivery in wireless networks

Providing quality-of-service (QoS) to video delivery in wireless networks has attracted intensive research over the years. A fundamental problem in this area is how to map QoS criterion at different layers and optimize QoS across the layers. In this paper, we investigate this problem and present a cross-layer mapping architecture for video transmission in wireless networks. There are several important building blocks in this architecture, among others, QoS interaction between video coding and transmission modules, QoS mapping mechanism, video quality adaptation, and source rate constraint derivation. We describe the design and algorithms for each building block, which either builds upon or extend the state-of-the-art algorithms that were developed without much considerations of other layers. Finally, we use simulation results to demonstrate the performance of the proposed architecture for progressive fine granularity scalability video transmission over time-varying and nonstationary wireless channel.
Recursive patching for video-on-demand (VOD) systems with limited client buffer constraint

To reduce the service bandwidth consumption of a video-on-demand (VOD) system, a recursive patching idea is proposed and several on-line algorithms based on this idea are experimented with in this research. With a limited receiver buffer space, we adopt a simple fixed-threshold starting rule, in which a full stream is initiated by using a fixed threshold. A control window is introduced to regulate the degree of service merging. Experimental results show that proposed online recursive patching schemes outperform the traditional graceful patching scheme by a wide margin even with a very small client buffer size. A moderate buffer size (i.e. 5 - 10 min) is large enough to maintain a good performance for proposed schemes.
Direct Techniques for Optimal Sub-Pixel Motion Accuracy Estimation and Position Prediction

Direct techniques for the optimal resolution estimation and position prediction of subpel motion vectors (MVs) based on integer-pel MVs are investigated in this paper. Although it is common to determine the optimal MV position by fitting a local error surface using integer-pel MVs, the characteristics of the error surface have not been thoroughly studied in the past. Here, we use an approximate condition number of the Hessian matrix of the error surface to characterize its shape in a local region. By exploiting this shape information, we propose a block-based subpel MV resolution estimation method that allows each block to choose its optimal subpel MV resolution for the optimal rate-distortion (R-D) performance adaptively. Furthermore, we propose two MV position prediction schemes for ill and well-conditioned error surfaces, respectively. All proposed techniques are direct methods, where no iteration is required. Experimental results are given to show the R-D performance of the proposed subpel MV resolution estimation and position prediction schemes.
Fast tree-structured nearest neighbor encoding for vector quantization

This work examines the nearest neighbor encoding problem with an unstructured codebook of arbitrary size and vector dimension. We propose a new tree-structured nearest neighbor encoding method that significantly reduces the complexity of the full-search method without any performance degradation in terms of distortion. Our method consists of efficient algorithms for constructing a binary tree for the codebook and nearest neighbor encoding by using this tree. Numerical experiments are given to demonstrate the performance of the proposed method.