nils-herrmann/clean_scirepeval huggingface dataset:
Improved image denoising with adaptive nonlocal means (ANL-means) algorithm

An adaptive nonlocal-means (ANL-means) algorithm for image denoising is proposed in this work. It employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique to achieve robust block classification in noisy images. Then, a local window is adaptively adjusted to match the local property of a block and a rotated matching algorithm that aligns the dominant orientation of a local region is adopted for similarity matching. Furthermore, the noise level is estimated using the block classification result and the Laplacian operator. Experimental results are given to demonstrate the superior denoising performance of the proposed ANL-means denoising technique over various image denoising benchmarks in terms of the PSNR value and perceptual quality comparison, where images corrupted by additive white Gaussian noise (AWGN) are tested.
Opportunistic Multicast Scheduling with Multiple Multicast Groups

The use of opportunistic multicast scheduling (OMS) for systems with multiple multicast groups is examined in this work. Here, a general downlink scenario where a single basestation is to transmit independent data streams to multiple groups of users is considered. Within each group, common information is transmitted, but between groups, the source information is independent. In the literature, the OMS scheme has been proposed for the case where all users belong to a single multicast group. This scheme allows the base-station to transmit at a higher rate in each time slot by scheduling the transmission to only a subset of users in the group. By encoding the data stream using fountain codes, each user will be able to decode the message whenever a sufficient number of data bits are received, regardless of which specific time slots the user was able to receive in. In this work, the use of OMS is extended to systems with multiple multicast groups and the so-called multicast throughput region is defined to characterize the performance of the multigroup OMS scheme. The analytical results based on extreme value theory are utilized to accurately predict the optimal multicast group-sizes and the optimal power allocation policy when maximizing the weighted sum throughput. By choosing the weights appropriately, it is shown that the method can be further utilized to ensure proportional fairness among the multiple multicast groups. The efficacy of the proposed OMS schemes is shown through numerical simulations.
Joint temporal-spatial rate control for adaptive video transcoding

A joint temporal-spatial rate control method is proposed for adaptive MPEG video transcoding in this work. The proposed rate control scheme adjusts both the frame rate and the frame quantization step size according to the available channel bandwidth to achieve good temporal-spatial quality tradeoff. In the proposed transcoding scheme, motion vectors are reused to facilitate the transcoding speed. Consequently, I and P frames are not skipped in order to maintain the prediction sequential order while some B frames containing more temporal redundancy may be skipped to save the bit rate for image quality enhancement. Skipped frames can be regenerated at the decoder to ensure the full frame rate playback. The TM7 quadratic Rate-Qtz model is adopted in the proposed scheme to calculate the re-quantization step size from a given target bit rate. Simulation results show that the proposed rate control scheme outperforms the MPEG-4 frame level rate control in the average PSNR value when applied to the context of MPEG-2 to MPEG-4 video transcoding. The complexity of the proposed transcoder remains low so that it can be used in real-time applications.
Joint Reconstruction and Registration Using Level Sets: Application to the Computer-Aided Ablation of Atrial Fibrillation

A novel surface reconstruction algorithm based on a variational framework using the level set method is proposed and its application to atrial fibrillation (AF) treatment is presented in this work. The need of reconstructing the cardiac surface from multi-modality data with magnetic resonance imaging (MRI) and computer tomography (CT) for surgical AF treatment is first explained. An implicit form of the cardiac surface is analytically derived by optimizing an energy functional that takes into account the distance between the surface and electroanatomic mapping (EAM) data and its similarity to a prior shape model. This problem is then solved numerically. The resultant algorithm allows non-rigid deformation to occur in the reconstruction of the cardiac surface. Promising numerical and experimental results show the potential of the proposed approach.
Speech content integrity verification integrated with ITU G.723.1 speech coding

A speech content integrity verification scheme integrated with ITU G.723.1 speech coding to minimize the total computational cost is proposed in this research. Speech features relevant to the semantic meaning are extracted, encrypted and attached as the header information. This scheme is not only much faster than cryptographic bitstream integrity algorithms, but also more compatible with a variable of applications. The speech signal could go through recompression, amplification, transcoding, re-sampling, D/A and A/D conversion and minor white noise pollution without triggering the verification alarm.
Iterative signal extrapolation algorithms with wavelets

A generalized Papoulis-Gerchberg (PG) algorithm for signal extrapolation based on the wavelet representation has been recently proposed by Xia, Kuo and Zhang. In this research, we first show that the generalized PG algorithm converges to the minimum norm solution when the wavelet bases are semi-orthogonal (or known as the prewavelet). However, the generalized PG algorithm converges slowly in numerical implementation. To accelerate the convergence rate, we formulate the discrete signal extrapolation problem os a two-step process and apply the steepest descent and conjugate gradient methods for its solution. Numerical experiments are given to illustrate the performance of the proposed algorithms.<<ETX>>
Complexity modeling of spatial and temporal compensations in H.264/AVC decoding

Complexity modeling of spatial-temporal compensations in H.264/AVC decoding is performed by examining a rich set of inter- and intra-prediction modes. Specifically, we study the relationship between motion vectors, frame sizes and properties of reference frames since they are related to cache management efficiency. The proposed models provide good estimation results for test video bit streams over a wide range of bit rates. As an application, an H.264/AVC encoder equipped with these models estimates the decoding complexity and chooses the best inter- or intra-prediction mode to meet the decoding complexity constraint of a target decoding platform. The decoding complexity of the resultant bit stream is reduced at the cost of small PSNR loss.
Low-variance TCP-friendly throughput estimation for congestion control of layered video multicast

A low-variance TCP-friendly throughput estimation scheme for robust congestion control of layered video transmission is presented in this work. We propose the inclusion of min-max averaging operation in the basic average loss interval (ALI) algorithm. The proposed operation helps reduce the variance of the ALI estimation by suppressing the correlation of neighboring loss intervals arising from the TCP additive-increase-multiplicative-decrease (AIMD) behavior. The resulting low-variance TCP throughput estimation scheme is applied to layered video multicast congestion control, and its performance is evaluated via computer simulation.
A fluid model for error propagation characterization in video coding

An error corruption model (ECM) to describe the interframe error propagation phenomenon in a motion-compensated predictive video codec using fluid flow characteristics is proposed. First, we derive a diffusion differential equation and discuss its solution, which captures the damping effect of error propagation. Then, we propose a tracking quadrilateral (TQ) mechanism to capture the shaping and drilling effects of error propagation. Finally, we integrate these building blocks to form an adaptive fluid-based ECM (F-ECM). The accuracy of the proposed F-ECM is verified by experimental results.
Fast Non-Local Means (NLM) Computation With Probabilistic Early Termination

A speed up technique for the non-local means (NLM) image denoising algorithm based on probabilistic early termination (PET) is proposed. A significant amount of computation in the NLM scheme is dedicated to the distortion calculation between pixel neighborhoods. The proposed PET scheme adopts a probability model to achieve early termination. Specifically, the distortion computation can be terminated and the corresponding contributing pixel can be rejected earlier, if the expected distortion value is too high to be of significance in weighted averaging. Performance comparative with several fast NLM schemes is provided to demonstrate the effectiveness of the proposed algorithm.
An improved DC recovery method from AC coefficients of DCT-transformed images

Motivated by the work of Uehara et al. , an improved method to recover DC coefficients from AC coefficients of DCT-transformed images is investigated in this work, which finds applications in cryptanalysis of selective multimedia encryption. The proposed under/over-flow rate minimization (FRM) method employs an optimization process to get a statistically more accurate estimation of unknown DC coefficients, thus achieving a better recovery performance. It was shown by experimental results based on 200 test images that the proposed DC recovery method significantly improves the quality of most recovered images in terms of the PSNR values and several state-of-the-art objective image quality assessment (IQA) metrics such as SSIM and MS-SSIM.
Complexity modeling of H.264/AVC CAVLC/UVLC entropy decoders

A complexity model for context-based adaptive variable length coding (CAVLC) and universal variable length coding (UVLC) in the H.264/AVC decoder is proposed. CAVLC and UVLC are used to code source data, i.e., quantized transformed coefficients (QTCs), and header data, respectively. CAVLC and UVLC are important in the baseline profile of the H.264/AVC codec since this profile targets at low complexity and low cost applications so that only CAVLC and UVLC are allowed to be the entropy coding tools. This model is verified experimentally. Possible applications of this model are also described. Since the entropy decoding complexity becomes significant in the high bit rate video due to more non-zero QTCs and motion vectors (MVs), one application scenario is to integrate this model into the H.264 encoder so that the encoder can estimate the possible decoding complexity and then select proper coding modes to generate the decoder-friendly bit streams, which are easy to decode for a particular decoding platform.
Rate-Distortion (RD) Analysis of Subpel Motion Vector Resolution Selection for Video Coding

A rate-distortion (RD) model for subpel motion vector (MV) selection is proposed for power-efficient video coding in this work. To reduce the encoder complexity, the block texture feature is used in the proposed RD model to predict the potential subpel MV gain for a given block. If the gain is not sufficiently large, we may choose integer or half-pel MV to save the search and interpolation cost at the encoder. Quantization noise and external noise are included in the model to improve the prediction accuracy furthermore. The resultant model is shown to be reliable in predicting the RD gain for different subpel resolutions. It is shown by experimental results that the proposed subpel MV selection scheme can maintain the RD performance similar to full subpel MV while reducing encoder complexity significantly.
Recovery of multiband signals using finite samples

In this research, we consider a special class of band-limited signals which have a multiband structure in the frequency domain, and propose a new reconstruction algorithm to exploit the multiband feature of the underlying signals. The concept of the critical value is introduced to measure the performance of a reconstruction algorithm. We show analytically that the new algorithm performs better than the MMSE estimator for band-limited/multiband signals in terms of the critical value and region measure. Numerical examples are given for performance comparison of various methods.<<ETX>>
Audiovisual-based adaptive speaker identification

An adaptive speaker identification system is presented in this paper, which aims to recognize speakers in feature films by exploiting both audio and visual cues. Specifically, the audio source is first analyzed to identify speakers using a likelihood-based approach. Meanwhile, the visual source is parsed to recognize talking faces using face detection/recognition and mouth tracking techniques. These two information sources are then integrated under a probabilistic framework for improved system performance. Moreover, to account for speakers' voice variations along time, we update their acoustic models on the fly by adapting to their newly contributed speech data. An average of 80% identification accuracy has been achieved on two test movies. This shows a promising future for the proposed audiovisual-based adaptive speaker identification approach.
Real-time content-based processing of multicast video

The access of high bandwidth multimedia data is generally limited to the network due to the limited network resources, inefficient indexing mechanism and less of semantic interpretations. Currently, most content-based video content representation involves the segmentation and indexing of video based on scene change and camera/object motion, and such research generally performs off-line video processing. Little research has been done on on-line video processing, which is crucial in video communication applications such as video conferencing, video multicasting and on-line video browsing and retrieval. This research investigates real-time content-based processing of multicast video over the Internet. New on-line video feature extraction schemes, such as scene change detection, on-line key frame classification, are considered to meet the requirement of real-time video multicasting filtering based on the user's profile over the Internet. The annotation and features extracted from a multicast videoconference bitstream by the on-line video content analysis proxies, which are using the proposed video processing algorithms, are output to a separate metadata channel for the further assistance of semantic multicasting of video content. The performance of the proposed algorithms is also demonstrated.
Adaptive motion estimation in video coding with a stochastic model

A new motion vector (MV) estimation method for video image compression based on a stochastic model is presented in this work. It is well known that the location of the smallest sum of absolute difference (SAD) does not always give the true MV since the MV obtained via full block search is often corrupted by noise. Thus, multiple locations of relatively small SAD are searched with an adaptive search window by using our method. We pick an MV among those candidates by using temporal correlation. Furthermore, since temporal correlation reveals the noise level in a particular region of the video image sequence, we are able to reduce the search area very effectively. The excellent performance of the proposed method is demonstrated by numerical experiments.<<ETX>>
Performance of asynchronous long-code multicarrier CDMA systems in the presence of correlated fading and inter-carrier interference

The effect of inter-carrier interference (ICI) and aperiodic random spreading sequences on the performance of asynchronous multicarrier code division multiple access (CDMA) systems with correlated fading is investigated in this research. Random parameters including asynchronous delays, correlated Rayleigh fading and spreading sequences are averaged to determine the unconditional covariance matrix of the interference-plus-noise vector. An analytic expression for the bit error probability is obtained based on the Gaussian approximation. Then, we extend the result by considering situations where eigenvalues are not necessarily all identical or distinct. Finally, design tradeoffs among the number of sub-carriers, fading correlations, ICI and multipath effect are presented in simulation results.
Anti-collusion fingerprinting with scalar Costa scheme (SCS) and colluder weight recovery

An anti-collusion fingerprinting system is developed to protect media files against time-varying collusion attacks based on the scalar Costa scheme (SCS) and colluder weight recovery. We treat the host signal as a parallel Gaussian channel and fingerprints as transmitted user signals. We decompose the Gaussian channel into multiple independent subchannels, and assign different user messages to different subchannels. Then, colluder weights in collusion attacks can be estimated using pilot symbols at the decoder, and all weights can be estimated and compensated. As a result, the decoding region on the parametric space can be recovered as an original format. It is shown by experimental results that the proposed fingerprinting system has excellent performance in colluder detection.
New PAR/NL scheme for stochastic texture interpolation

A texture interpolation technique based on the locally piecewise auto-regressive (PAR) model and the non-local (NL) training procedure is investigated in this work. The proposed PAR/NL scheme selects model parameters adaptively based on local image properties with an objective to improve the interpolation performance of nonadaptive models, e.g., the bicubic algorithm. To determine model parameters for stochastic texture, we use the non-local (NL) learning algorithm to update and refine these local model parameters under the assumption that the PAR model parameters are self-regular. As compared to previous interpolation algorithms, the proposed PAR/NL scheme boosts texture details, and eliminates blurring artifacts perceptually. Experimental results are given to demonstrate the performance of the proposed technique.
MAI-Free MC-CDMA Systems Based on Hadamard&#8211;Walsh Codes

It is known that multicarrier code-division multiple-access (MC-CDMA) systems suffer from multiaccess interference (MAI) when the channel is frequency-selective fading. In this paper, we propose a Hadamard-Walsh code-based MC-CDMA system that achieves zero MAI over a frequency-selective fading channel. In particular, we will use appropriately chosen subsets of Hadamard-Walsh code as codewords. For a multipath channel of length L, we partition a Hadamard-Walsh code of size N into G subsets, where G is a power of two with GgesL. We will show that the N/G codewords in any of the G subsets yields an MAI-free system. That is, the number of MAI-free users for each codeword subset is N/G. Furthermore, the system has the additional advantage that it is robust to carrier frequency offset (CFO) in a multipath environment. It is also shown that the MAI-free property allows us to estimate the channel of each user separately and the system can perform channel estimation much more easily. Owing to the MAI-free property, every user can enjoy a channel diversity gain of order L to improve the bit error performance. Finally, we discuss a code priority scheme for a heavily loaded system. Simulation results are given to demonstrate the advantages of the proposed code and code priority schemes
Musical Onset Detection Based on Adaptive Linear Prediction

A new musical onset detection technique based on adaptive linear prediction theory is proposed in this work. We decompose a music signal into multiple sub-bands, and then apply a forward linear prediction error filter (LPEF) to model the narrow-band signal in each band, respectively. To enhance the modeling performance, the coefficients of the LPEF are updated with the least-mean-squares (LMS) algorithm. Under this framework, the onset detection problem can be formulated as the peak-error location problem. Peak selection algorithms are applied to prediction errors to locate the onset time. It is shown by experimental results that the proposed algorithm outperforms several well known existing methods for onset detection
Synthesis-Based Texture Video Coding With Side Information

The addition of a new component to the traditional synthesis-based texture video coding algorithm is investigated in this paper. That is, we add the side information in the form of low-quality video to enhance the texture video synthesis performance with reducing the unpleasant mismatch between analyzed and synthesized regions. As compared with the conventional synthesis algorithm, our algorithm is more flexible since the behavior and quality of the output texture can be adjusted by the amount of the side information, which is determined by the user. To this end, we develop an area-adaptive side information selection scheme that chooses the proper amount of the side information for a given bit budget. Furthermore, we propose a texture decomposition scheme that extracts the non-synthesizable illumination component from the source video for separate coding so as to maximize the synthesis functionality. The superior performance of the proposed texture video synthesis technique is demonstrated by several coding examples.
Ultra-Wide Band Impulse Radio (UWB-IR) with Super-Orthogonal Turbo Codes (SOTC)

The performance of a direct-sequence ultra-wide- band (DS-UWB) system coded by super-orthogonal turbo codes (SOTC) over simulated UWB channels is studied in this work. The SOTC scheme is attractive due to its low rate and low- decoding-complexity features. Several receivers are compared for data detection over an indoor IEEE 802.15.3a channel, including the SOTC encoded matched-filter (MF) receiver and the RAKE receiver. The performance of the SOTC-encoded UWB system with different receivers is evaluated by computer simulation. It is shown that the proposed system can achieve significant performance improvement over the DS-UWB system encoded by conventional repetition codes (RC).
Paradigm shifts in modern ICT era and future trends

Information and communication technologies (ICT) have become more mature after its development for more than half a century. We are in a new era in which ICT research has shifted its focus from technology development to novel applications. For example, digital technologies have been widely applied to speech, audio, video and graphics in various commercial applications today. Furthermore, the availability of the broadband wired/wireless Internet infrastructure and the cloud computing facilities has changed the way of web-based services. This lecture will address the following four major paradigm shifts in this modern IT era: • From the analog implementation to the digital implementation • From PC-centric to cloud/network-centric • From one-way broadcasting to two-way interaction • From HW/SW/infra-structure provision to contents and value-added services
Analysis of multihypothesis motion compensated prediction (MHMCP) for robust visual communication

A multihypothesis motion compensated prediction (MHMCP) scheme, which predicts a block from a weighted superposition of more than one reference blocks, is proposed and analyzed for error resilient visual communication in this research. By combining these reference blocks effectively, MHMCP can enhance the error resilient capability of compressed video as well as achieve a coding gain. In particular, we investigate the error propagation effect in the MHMCP coder and analyze the rate-distortion performance in terms of the hypothesis number and hypothesis coefficients. It is shown that MHMCP suppresses the short-term effect of error propagation more effectively than the intra-refreshing scheme.