nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Temporal Search Range Prediction for Motion Estimation in H.264

An efficient search range prediction method is proposed to reduce the complexity of motion search in the H.264 video coding standard in this work. The main idea is to predict the temporal search range by modeling the relationship between the rate-distortion (RD) coding gain and the required computational complexity. The proposed algorithm first predicts the temporal search range to maximize the ratio of the expected RD coding gain and the normalized computational cost. Then, fast motion search is performed within the predicted search range with some early termination rule. Experimental results show that the proposed algorithm can save approximately 63-75% of the encoding complexity in motion estimation of H.264 (JM9.3) with negligible degradation in the RD performance
Image compression with a hybrid wavelet-fractal coder

A hybrid wavelet-fractal coder (WFC) for image compression is proposed. The WFC uses the fractal contractive mapping to predict the wavelet coefficients of the higher resolution from those of the lower resolution and then encode the prediction residue with a bitplane wavelet coder. The fractal prediction is adaptively applied only to regions where the rate saving offered by fractal prediction justifies its overhead. A rate-distortion criterion is derived to evaluate the fractal rate saving and used to select the optimal fractal parameter set for WFC. The superior performance of the WFC is demonstrated with extensive experimental results.
Power control for packet-based wireless communication systems

A power control scheme for packet-based wireless communication systems is proposed in this paper. The proposed power control scheme aims at minimizing the total number of transmissions that a packet needs before it is received successfully over a Rayleigh fading channel subject to two constraints. One is that the transmission power should be greater than zero. The other is imposed by the constraint on the total transmission power at the base station. We use the augmented Lagrangian multiplier method to solve this problem and provide a theoretical solution. The simulation results show that, with the proposed power control scheme, the number of re-transmission can be reduced and the delay of a packet wasted in the channel can be decreased as well.
Power-aware topology control for wireless ad-hoc networks

A power-aware approach in the context of topology control that allows the power consumption to be evenly distributed among network nodes, and thereby prolongs the network lifetime, is proposed in this research. Unlike power-aware routing schemes, where exact traffic flow and residual energy level of each node are required, our power-aware topology control only requires the residual energy levels and location information of the reachable neighboring nodes. When a node is making a decision on whether a wireless link between itself and a reachable neighboring node should be preserved in the topology being constructed, the decision is made based on not only the distance from its neighboring nodes but also the residual energy levels of itself and its neighboring nodes. Also, the topology is restructured from time to time based on the residual energy level of each node. The performance improvement by our power-aware topology control algorithm is shown through extensive simulations
Space-time blind delay and DOA estimation in chip-asynchronous DS-CDMA systems

A blind time-delay and direction-of-arrival (DOA) estimation algorithm using beamforming arrays is proposed for short-code chip-asynchronous DS-CDMA systems. The proposed space-time processing technique consists of two stages. The time delay estimation process is performed at the first stage. Then, the soft information obtained from the first stage is used for DOA estimation at the second stage. The time-delay estimation algorithm is based on the minimum output energy (MOE) criterion. It consists of two steps, coarse and fine synchronization, where integer and fractional delays of the desired signal are estimated, respectively. The DOA estimation is performed based on the estimated power of the desired signal (or soft information) from each beamforming filter steering at a different direction. Experiments are conducted to demonstrate the superior performance of the proposed scheme.
Design issues on request migration for video-on-demand services

A random early migration (REM) algorithm is proposed to handle the request migration in a video-on-demand (VoD) server system. When a new request is dispatched to a media server, REM compares the current service load with thresholds and decides whether request migration is needed with a certain probability, which is a function of the service load. To facilitate the performance analysis of REM, we introduce a state matrix structure to record the current system load status. Based on the state matrix structure, we develop a state transition method to study the parameter effect on the REM performance and compare the real time performance between REM and traditional migration with early start (TMES). The simulation result shows that, in terms of the migration cost, REM can achieve a more enhanced performance than the traditional migration scheme. Compared to TMES, REM introduces smoother migrations between media servers and thus leads to a lower real-time system load.
A suffix array approach to video copy detection in video sharing social networks

To address the multiplicity and copyright issues on file sharing social networks, we propose a fast video copy detection algorithm using the suffix array data structure in this work. The proposed algorithm consists of two steps. In the first step, we extract robust features which are discriminative yet insensitive to various attacks. Specifically, we develop a compact one-dimensional signature based on the shot change position of video files. Unlike images and audio, the size of a video file is usually large, which makes it computationally expensive to match two long signature sequences. Thus, in the second step, we adopt an efficient matching technique based on the suffix array data structure. The proposed system can perform the sequence matching in linear time while the complexity of conventional duplicate video detection algorithms grows at least quadratically with the video length.
Measure and Prediction of HEVC Perceptually Lossy/Lossless Boundary QP Values

Evaluation of coding efficiency is traditionally modeled as a continuous rate-distortion (R-D) function, where the peak signal-to-noise ratio (PSNR) is adopted as the quality measure. Although the PSNR-versus-bitrate curve offers some useful tradeoff information between video quality and coding bit-rates, it does not take human perceptual experience into account. In this work, by following the recent image/video quality assessment framework based on the just-noticeable-difference (JND) notion, we conduct a subjective test for HEVC (High Efficiency Video Codec) video to measure the QP value that lies in the boundary of perceptually lossless and lossy coded bit streams for each human subject. This is also known as the first JND point. It is observed that the statistics of the first JND points of 30 subjects follows the normal distribution for a great majority of test sequences. Finally, a machine-learning approach is proposed to predict the mean of the group-based JND distribution based on extracted video features. It is shown by experimental results that the mean JND point can be predicted accurately.
Unsupervised Clustering Guided Semantic Segmentation

With the development of Fully Convolutional Neural Network (FCN), there have been progressive advances in the field of semantic segmentation in recent years. The FCN-based solutions are able to summarize features across training images and generate matching templates for the desired object classes, yet they overlook intra-class difference (ICD) among multiple instances in the same class. In this work, we present a novel fine-to-coarse learning (FCL) procedure, which first guides the network with designed 'finer' sub-class labels, whose decisions are mapped to the original 'coarse' object category through end-to-end learning. A sub-class labeling strategy is designed with unsupervised clustering upon deep convolutional features, and the proposed FCL procedure enables a balance between the fine-scale (i.e. sub-class) and the coarse-scale (i.e. class) knowledge. We conduct extensive experiments on several popular datasets, including PASCAL VOC, Context, Person-Part and NYUDepth-v2 to demonstrate the advantage of learning finer sub-classes and the potential to guide the learning of deep networks with unsupervised clustering.
An approximately MAI-free multiaccess OFDM system in carrier frequency offset environment

The carrier frequency offset (CFO) effect of a new multiaccess orthogonal frequency division multiplexing (OFDM) transceiver is analyzed. We show that multiaccess interference (MAI) due to CFO can be mitigated by choosing a proper set of orthogonal codes. That is, if we use M/2 symmetric or antisymmetric codewords of the M Hadamard-Walsh codewords, MAI can be greatly reduced to a negligible amount. The new OFDM system has been shown to be approximately MAI-free when there is no CFO. The new result developed here shows that the system continues to be approximately MAI-free even in the presence of CFO. Furthermore, we derive a close form for the reduced MAI, which reveals an interesting property of the system. That is, when the number of users increases, the reduced MAI and the inter carrier interference (ICI) due to self-CFO will decrease. Finally, it is demonstrated by simulation that the proposed OFDM scheme with proper code selection outperforms the OFDMA and the multicarrier code division multiplexing access (MC-CDMA) systems in a CFO environment.
A semantic classification and composite indexing approach to robust image retrieval

This paper investigates the use of image content analysis and image clustering techniques to organize an image database and to determine low-level features and semantic meanings for indexing and retrieval. A robust image retrieval system consisting of semantic classification, composite indexing and interactive query is proposed under this framework. In this system, a large image collection with great varieties is categorized into different classes according to distinct characteristics. The semantics of feature descriptors and the relationship between feature descriptors and image contents are then explored. Finally, a composite indexing and interactive retrieval procedure using low-level features and high-level understanding is developed to achieve a robust image query performance.
Robust and Flexible Wireless Video Multicast with Network Coding

A robust and flexible wireless video multicast system with the H.264 scalable video coding (SVC) coding format and network coding (NC) is proposed in this work. The system enables efficient video streaming to heterogeneous receivers in packet erasure channels. NC is used to simplify erasure protection as well as the complexity of multicast tree construction and maintenance. It is shown by simulation results that the proposed solution improves PSNR over the traditional video multicast with error correction codes (ECC) in store-and-forward networks.
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 in this research. A music 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 front-end for such 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 objects, and an overall correct recognition rate of around 80% is demonstrated.
Bit Allocation for Spatial Scalability Coding of H.264/SVC With Dependent Rate-Distortion Analysis

We propose a model-based spatial layer bit allocation algorithm for H.264/scalable video coding (SVC) in this paper. The challenge of this problem lies in the fact that the rate-distortion (R-D) behavior of an enhancement layer is dependent on its preceding layers because of inter-layer prediction. To solve it, we first focus on the case of two spatial layers, derive the distortion and rate models of the dependent layer analytically, and develop a low-complexity bit allocation algorithm. It is shown by experimental results that the proposed two-layer bit allocation algorithm can achieve the coding performance close to the optimal R-D performance based on the full search method. Then, we extend this result to multilayer bit allocation by performing the two-layer allocation scheme recursively. Finally, we compare the performance of group of pictures-based and frame-based spatial layer bit allocation schemes at a fixed temporal resolution. The superior performance of the proposed spatial layer bit allocation algorithm is demonstrated using Joint Scalable Video Model reference software algorithm and two prior H.264/SVC rate control algorithms as the benchmarks.
Robust EBCOT coding technique for wireless image transmission

Embedded block coding with optimized truncation (EBCOT) is an efficient and scalable wavelet-based image coding technique, which divides each subband in independently coded blocks. It is therefore more error resilient than many other wavelet based schemes. However, the loss of data of a block in any lower frequency subband in EBCOT can still degrade the perceptual image quality considerably. This paper investigates the use of reversible variable length codes (RVLC) and data partitioning for the coding of coefficients of low frequency subbands in EBCOT (instead of arithmetic codes). RVLCs are known to have a superior error recovery property due to their two-way decoding capability. It is demonstrated that the proposed scheme requires only a small amount of overhead in the bit-rate, but provides a significantly improved performance in the presence of errors.
Feature-based intra-prediction mode decision for H.264

A fast mode decision method for intra prediction in H.264 is proposed in this work to reduce the encoder complexity. The proposed algorithm adopts a multistage sequential mode decision process that uses joint spatial and transform domain features to filter out unlikely candidate modes and. in the final stage, a simplified rate-distortion optimization method with a rate-distortion model is applied to a very limited number of candidates. It incorporates an early termination mechanism to save computations. Experimental results are given to demonstrate that the proposed scheme achieves a speed-up factor of 10-15 as compared with the current RD optimized mode decision with little quality degradation.
Sequential modulation classification of dependent samples

We classify the modulation scheme of a received signal waveform modeled by a finite state Markov chain. We compare the likelihood ratio test (LRT) known as a fixed-sample-size classifier, which uses a fixed amount of data, and the sequential probability ratio test (SPRT) known as a fixed-error-rate classifier, which uses a variable amount of data just enough to achieve a certain correct rate. The SPRT approach has several advantages, including reduced computational complexity, less decision delay, controllable classification error rate, etc. The performance of MPSK and trellis-coded modulation (TCM) classifiers are demonstrated.
Cartoon animation and morphing by using the wavelet curve descriptor

Proposes a framework for cartoon animation and morphing by using a hierarchical planar curve descriptor based on the wavelet transform. To facilitate the animation task, the authors first model the motion of curves with the Lagrangian dynamic equation where the multiscale curve is driven by internal and external forces. The spatial and frequency localization property of the wavelet representation results in a virtually decoupled Lagrangian equation and, as a result, the computation is greatly simplified. The motion parameters, which contain the kinematic information of the control points, are extracted from motions of real video images. They are then used to generate motion sequences of similar nature. The authors also examine a highly automatic algorithm for non-self-intersecting contour morphing. Experiments of the proposed algorithms are conducted to demonstrate their performance.<<ETX>>
Direct shape from texture using a parametric surface model and an adaptive filtering technique

In this research, we propose a new iterative shape from texture (SFT) algorithm which extracts accurate surface depth information of a curved object covered with fairly homogeneous texture directly. The shape information can be inferred from the rate of texture distortion depicted in an image, and therefore the modeling of the projection and surface geometry as well as the estimation of local texture variation are crucial in obtaining accurate surface shape of an object. By introducing semi-perspective projection camera model and a parametric surface model, we establish a new SFT problem formulation called the textural irradiance equation which relates the local texture density called textural intensity to finite surface parameters. Moreover, by adopting an adaptive multiscale filtering scheme for local texture density estimation, in which the scale or frequency band of a local edge filter is chosen adaptively according to the local shape information, we greatly enhance the accuracy of the estimation of the projected local texture densities, and the final reconstructed shape. We demonstrate the performance of the proposed algorithm by the test with several synthetic and real texture images.
Joint connection-level and packet-level quality-of-service support for VBR traffic in wireless multimedia networks

In this paper, we investigate call admission control (CAC) schemes that can jointly provide connection-level quality-of-service (QoS) (in terms of the new call blocking probability and the handoff dropping probability) and packet-level QoS (in terms of the packet loss probability) for wireless multimedia networks. Stationary CAC schemes are proposed as the results of the solution to constrained optimization problems. A dynamic CAC scheme that can be adapted to varied and varying traffic conditions dynamically is also proposed. The proposed CAC schemes are computationally efficient and easy to implement, thus being suitable for real-time system deployment. Simulation results have demonstrated that the proposed dynamic CAC scheme achieves better performance when applied to realistic traffic conditions found in wireless multimedia networks.
Multicast video synchronization via MPEG-4 FGS/XML representation

A solution to the re-synchronization problem in the layered video multicast, based on the MPEG-4 FGS/XML representation, is proposed. A raw video file is encoded into the MPEG-4 FGS format that consists of the base layer and enhancement layers. The corresponding XML trees are built using the integrated MPEG-4 FGS/XML parser simultaneously. The XML representation of MPEG-4 FGS video presents an accurate and randomly accessible tree structure to interact with the standard MPEG-4 encoder/decoder. Under the assumption that the XML documents can be transmitted via the reliable TCP protocol at the same time when the MPEG-4 FGS bitstream is delivered, we can demonstrate that the difficult resynchronization problem in layered video multicast can be easily tackled with the assistance of the XML description.
An ECM-based receiver for OFDM signals affected by carrier frequency offsets

A receiver based on the expectation-conditional maximization (ECM) algorithm is presented to detect OFDM signals that are affected by carrier frequency offsets over time- varying multipath channels. The proposed scheme achieves joint channel estimation, frequency synchronization and data detection in an iterative fashion. Since the transmitted data at each iteration are recovered based on suitable channel and frequency estimates as obtained from the previous step, the new scheme is computationally efficient. It operates on a symbol-by-symbol basis for uncoded transmissions, whereas it can be implemented by modifying the branch metrics of a Viterbi decoder in case of trellis-coded modulations. Computer simulations confirm the effectiveness of the proposed receiver.
SPCp1-03: Orthogonal Codes for MAI-Free MC-CDMA with Carrier Frequency Offsets (CFO)

The performance of MC-CDMA in the presence of carrier frequency offsets (CFO) can be severely degraded due to multiaccess interference (MAI). It is shown in this work that a properly chosen subset of real Hadamard-Walsh or exponential codes can achieve zero MAI in a CFO environment. For a channel of length L and a number G of power of 2 with G ges L, we prove that 1 + log2 (iV/G) Hadamard-Walsh codewords or N/G exponential codewords can achieve zero MAI under any CFO level. Simulation results are given to corroborate derived theoretical results and evaluate the performance of MC-CDMA with a variable number of users in a CFO environment.
Spatial and Temporal Error Concealment Techniques for Video Transmission Over Noisy Channels

Two novel error concealment techniques are proposed for video transmission over noisy channels in this work. First, we present a spatial error concealment method to compensate a lost macroblock in intra-coded frames, in which no useful temporal information is available. Based on selective directional interpolation, our method can recover both smooth and edge areas efficiently. Second, we examine a dynamic mode-weighted error concealment method for replenishing missing pixels in a lost macroblock of inter-coded frames. Our method adopts a decoder-based error tracking model and combines several concealment modes adaptively to minimize the mean square error of each pixel. The method is capable of concealing lost packets as well as reducing the error propagation effect. Extensive simulations have been performed to demonstrate the performance of the proposed methods in error-prone environments
Techniques for movie content analysis and skimming: tutorial and overview on video abstraction techniques

With the proliferation of digital video, video summarization and skimming has become an indispensable tool of any practical video content management system. This paper provides a tutorial on the existing abstraction work for generic videos and presents state-of-the-art techniques for feature film skimming. The paper also describes the authors' recent work on movie skimming using audiovisual tempo analysis and specific cinematic rules. With the maturity of the movie genre classification, content understanding and video abstraction techniques, an automatic movie content analysis system that facilitates navigation, browsing, and search of desired movie content is possible in the near future