nils-herrmann/clean_scirepeval huggingface dataset:
Towards efficient and scalable speech compression schemes for robust speech recognition applications

This paper presents a scheme for distributed automatic speech recognition. A hidden Markov model (HMM)-based speech recognition system with a mel frequency cepstral coefficients (MFCC) front end was used in the evaluation. The goal was to achieve good recognition performance while compressing the MFCC feature vectors. Compression rates and recognition performance for both a digit and an alphabet database are reported. Compared to a scheme of recognizing speech encoded by low bit rate encoders, and previously reported schemes, our method can achieve good recognition performance with bit rates lower than 1 kbps, using low encoding complexity. The encoding algorithms developed are scalable, allowing bit rate and recognition performance trade-offs, and can be combined with unequal error protection or prioritization to allow graceful degradation of performance in the presence of channel errors.
Edge-preserving depth-map coding using graph-based wavelets

In order to efficiently encode depth map images in a multi-view video coding scenario, two basic properties of these images can be leveraged: first, errors in pixels located near the edges of objects have a greater perceptual impact on the synthesized view; second, depth maps can be approximated as piecewise planar signals. We make use of these facts to define a discrete wavelet transform using lifting that avoids filtering across edges. The filters are designed to fit the planar shape of the signal. This leads to an efficient representation of the image while preserving the edge information. By preserving the edge information, we are able to improve the quality of the synthesized views, as compared to existing methods.
Motion Estimation at the Decoder Using Maximum Likelihood Techniques for Distributed Video Coding

Distributed video coding techniques have been proposed to support relatively "light" video encoding systems, where some of the encoder complexity is transferred to the decoder. In some of these systems, motion estimation is performed at the decoder to improve compression performance: a block in a previous frame has to be found that provides the correct side information to decode information in the current frame. In this paper we compare various techniques for motion estimation at the decoder that have been proposed in the literature and we propose a novel technique that exploits all the information available at the decoder using a maximum likelihood formulation. Our experiments show that likelihood techniques provide potential performance advantages when used in combination with some existing methods, in particular as they do not require additional rate overhead
Disc-GLasso: Discriminative graph learning with sparsity regularization

Learning graph topology from data is challenging. Previous work leads to learning graphs on which the graph signals used for training are smooth. In this paper, we propose an optimization framework for learning multiple graphs, each associated to a class of signals, such that representation of signals within a class and discrimination of signals in different classes are both taken into consideration. A Fisher-LDA-like term is included in the optimization objective function in addition to the conventional Gaussian ML objective. A block coordinate descent algorithm is then developed to estimate optimal graphs for different categories of signals, which are then used to efficiently classify the different signals. Experiments on synthetic data demonstrate that our proposed method can achieve better discrimination between the learned graphs, leading to improvements in subsequent classification tasks.
Technique for application of hi-bright LED in automobile industry through intelligent systems

The advantages offered by the electronic component LED (light emitting diode) have caused a quick and wide application of this device in replacement of incandescent lights. However, in its combined application, the relationship between the design variables and the desired effect or result is very complex and it becomes difficult to model by conventional techniques. This work consists of the development of a technique, through artificial neural networks, to make possible to obtain the luminous intensity values of brake lights using LEDs from design data.
Optimal scheduling for streaming of scalable media

Scalable, or layered, media representation appears to be more suitable for transmission over the current heterogeneous networks. In this paper we study the problem of scalable layered streaming media delivery over a lossy channel. The goal is to find an optimal transmission policy to achieve the best playback quality at the client end. The problem involves some trade-offs such as time-constrained delivery and data dependencies. For example, a layer should be dropped before transmission if it already has a delay such that it cannot be played before its scheduled time. Moreover, less important layers with near-playback-time may also be dropped or delayed for delivery in order to save bandwidth for other layers with a high priority. We propose a framework for scalable streaming media delivery, that involves a novel scheduling algorithm called Expected runtime Distortion Based Scheduling (EDBS) which decides the order in which packets should be transmitted in order to improve client playback quality in the presence of channel losses. A fast greedy search algorithm is presented that achieves almost the same performance as an exhaustive search technique (98% of the time it results in the same schedule) with very low complexity and is applicable for real-time application.
Rate control algorithms for video storage on disk based video servers

The emerging video-on-demand servers are equipped with large capacity and high speed disks, but the disk seeking time can be significant (in the case when several users access different data simultaneously). Studies show different approaches to reduce the disk seek time in order to increase the number of users that can be served simultaneously. Most of them provide strategies for disk storage (disk placement) of the video data. Since most video data is encoded with variable bit rate (VBR), using fixed-rate disk placement strategies may result in reduced quality (PSNR). We analyze the impact of disk placement strategies on the performance of the VBR video stream. We then show that the performance can be improved by using rate control techniques that are aware of the disk placement constraints, so that the VBR video source stream can be optimized for those constraints. This optimization (which can be performed off-line) results is quality improvements of 0.5 dB to 1.5 dB in our experiments.
Reduced complexity quantization under classification constraints

In optimal product vector quantization (VQ) sub-vectors within a vector are encoded separately. Optimal product VQ (PVQ) aims at maximizing the rate-distortion (RD) performance. We consider scenarios where PVQ is used to approximate the labeling obtained from an existing higher dimension quantizer or classifier. We present an efficient design technique under the labeling constraints and we show that performance is significantly improved if these are taken into account. We present two examples where this technique can be used. First we consider a PVQ designed to approximate a higher dimension classifier. In this case we show that with a small penalty in distortion (e.g., 0.04 dB loss) we can reduce significantly the misclassification (e.g., 48% relative reduction, 4.6% absolute reduction) with respect to a standard PVQ design. In our second example we show how hierarchical VQ (HVQ) can be used as a preprocessing stage for a standard unstructured VQ such that the HVQ stage enables a significant reduction of the codeword candidates to be searched in the VQ stage. Here again we show how HVQ designed to optimize the labeling enables a further reduction in complexity as the HVQ partition is designed to approximate the standard VQ partition.
Spectral anomaly detection using graph-based filtering for wireless sensor networks

This paper introduces a novel spectral anomaly detection method by developing a graph-based filtering framework. In particular, we consider the problem of unsupervised data anomaly detection over wireless sensor networks (WSNs) where sensor measurements are represented as signals on a graph. In our framework, graphs are chosen to capture useful proximity information about measured data. The associated graph-based filters are then employed to project the graph signals on normal and anomaly subspaces, and resulting projections are used in detection of data anomalies. The proposed approach has two main advantages over the standard spectral technique, principal component analysis (PCA). Firstly, graph-based filtering allows us to incorporate structural information known a priori (e.g., distance between sensors) in addition to data. Secondly, it provides localized transformations leading to effective distributed anomaly detection. Our experimental results show that our proposed solution outperforms PCA-based and distributed clustering-based anomaly detection methods in terms of receiver operating characteristics (ROCs).
A comparison of different haptic compression techniques

Immersive environments provide an artificial world to surround users. These environments consist of a composition of various types of immersidata: unique data types that are combined to render a virtual experience. To construct such an environment, immersidata acquisition is indispensable for storage and future query. However, this is challenging because of the real time demands and sizeable amounts of data to be managed. We propose and evaluate alternative techniques for achieving efficient sampling and compression of one immersidata type, the haptic data, which describes the movement, rotation, and force associated with user-directed objects in an immersive environment. Our experiments identify the benefits and limitations of various techniques in terms of their data storage, bandwidth and accuracy.
Asymptotic justification of bandlimited interpolation of graph signals for semi-supervised learning

Graph-based methods play an important role in unsupervised and semi-supervised learning tasks by taking into account the underlying geometry of the data set. In this paper, we consider a statistical setting for semi-supervised learning and provide a formal justification of the recently introduced framework of bandlimited interpolation of graph signals. Our analysis leads to the interpretation that, given enough labeled data, this method is very closely related to a constrained low density separation problem as the number of data points tends to infinity. We demonstrate the practical utility of our results through simple experiments.
A Model-Based Approach to Correlation Estimation Inwavelet-Based Distributed Source Coding with Application to Hyperspectral Imagery

In many practical distributed source coding (DSC) applications correlation information has to be obtained at the encoder in order to determine the encoding rate. Coding efficiency depends strongly on the accuracy of this correlation estimation, which often has to be performed under rate and complexity constraints. In this paper we focus on correlation estimation for wavelet-based DSC. We extend our previously proposed model-based estimation techniques, which provided accurate estimates of bit-plane level correlation under rate constraints, in the simple case where bit-planes are generated from the binary representation of the sources. To extend the model-based approach to wavelet-based DSC, we need to address two issues. Firstly, in order to improve coding efficiency, bit-planes are typically generated by more sophisticated algorithms in wavelet-based DSC (e.g., by deciding on the bitplane scan order based on coefficient "significance"), which makes model-based estimation more challenging. Secondly, certain wavelet subbands may not have enough coefficients for reliable model estimation, so that model-based techniques alone may not be sufficiently accurate. We propose solutions to these problems and, using a DSC-based hyperspectral image system as example, we demonstrate that model-based estimation can lead to efficient system implementation with lower computational and data exchange requirements, and improved parallelism, while incurring only small degradation in coding efficiency.
Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

Transformations on graphs can provide compact representations of signals with many applications in denoising, feature extraction, or compression. In particular, lifting transforms have the advantage of being critically sampled and invertible by construction, but the efficiency of the transform depends on the choice of a good bipartition of the graph into update (<inline-formula><tex-math notation="LaTeX">$\mathcal{U}$</tex-math></inline-formula>) and prediction (<inline-formula><tex-math notation="LaTeX">$\mathcal{P}$</tex-math></inline-formula>) nodes. This is the update/prediction (<inline-formula><tex-math notation="LaTeX">$\mathcal{U}/\mathcal{P}$</tex-math></inline-formula>) assignment problem, which is the focus of this paper. We analyze this problem theoretically and derive an optimal <inline-formula><tex-math notation="LaTeX">$\mathcal{U}/\mathcal{P}$</tex-math></inline-formula> assignment under assumptions about signal model and filters. Furthermore, we prove that the best <inline-formula> <tex-math notation="LaTeX">$\mathcal{U}/\mathcal{P}$</tex-math></inline-formula> partition is related to the correlation between nodes on the graph and is not the one that minimizes the number of conflicts (connections between nodes of same label) or maximizes the weight of the cut. We also provide experimental results in randomly generated graph signals and real data from image and video signals that validate our theoretical conclusions, demonstrating improved performance over state-of-the-art solutions for this problem.
Embedded image-domain compression using context models

Transform coding techniques are popular for their excellent lossy compression for natural images but fall short with "simple" images, i.e. images represented by a sparse subset of the available intensity values. In this paper we propose a novel image-domain compression technique aiming at simple images. The proposed method is a context-adaptive bitplane coder where each bitplane is encoded using a binary arithmetic coder. Our novel context modeling scheme allows to exploit the strong correlation across the bitplanes of simple images. Together with the scalable bitplane reduction technique proposed for increased compression efficiency, our bitplane coder produces an SNR-scalable embedded bitstream that is lossless when completely decoded.
Fast long-term motion estimation for H.264 using multiresolution search

In this paper, we propose an adaptive motion search window location algorithm which incorporates multiresolution motion estimation information and the motion vector information predicted from neighboring encoded blocks. To reduce the computational complexity, we employ the spatial and temporal search range reduction that we proposed in H. Chung, A. Ortega (2002). Our proposed fast search is implemented and tested in the emerging H.264 encoder reference software (JM5.0c). The proposed motion estimation algorithm is implemented in the low-complexity mode of motion estimation and mode decision (T. Wiegand (2002)). We compare the proposed algorithm with the full-search method which checks the distortion values of all the candidates in the given search area. With respect to the full-search method, our proposed algorithm has two advantages. First, due to the spatial and temporal search range reduction, our algorithm is faster. Second, due to the multiresolution search, our proposed algorithm can use larger motion search range with small additional complexity. The proposed algorithm can significantly reduce the computational complexity of motion estimation with slight increase in bit rate as compared to a full-search that covers the same motion range over which our proposed fast search operates. The proposed algorithm provides particularly significant gains for video sequences that contain large motions.
Distortion/decoding time tradeoffs in software DCT-based image coding

We present a general framework for variable complexity algorithms (VCA) and study the related issue of defining a minimum average complexity implementation. As an example we consider implementations of the inverse DCT (IDCT) which minimize the average computation time by taking advantage of the sparseness of the quantized input data. Since the decoding speed depends on the number of zeros in the input we then present a formulation that enables the encoder to optimize its quantizer selection so as to meet a prescribed "decoding time budget". This leads to a complexity-distortion optimization technique which is analogous to well known techniques for rate-distortion optimization. In our experiments we demonstrate significant reductions in decoding time.
An efficient compression method for one-shot multispectral camera

Many RGB digital cameras use a Bayer color filter array. In these cameras a full-resolution image is obtained from a mosaicked image by interpolation, and then the full resolution image is encoded. Similarly, mosaicked color filter arrays and interpolation methods for multispectral images have been proposed in recent years. The resulting full-resolution mutispectral images need to be encoded efficiently. This paper presents a new image compression method for one-shot multispectral imaging system. The proposed approach encodes the mosaicked multi-spectral image before interpolation. The decoder interpolates the image after decoding to reconstruct a full-resolution image. The simulation results show that the proposed method outperforms a conventional method, which encodes the full-resolution image after interpolation, at almost all bit rates. We also propose a heuristic color filter array design for our method that leads to lower bit rate.
Edge-aware intra prediction for depth-map coding

This work proposes a new intra prediction coding scheme for depth map images used in view interpolation. The main goal is to design a prediction scheme which can reduce the prediction error energy in blocks with arbitrary edge shapes. This will reduce the rate needed to encode such blocks while also eliminating some of the annoying artifacts caused by quantization. Since depth maps typically consist of smooth regions separated by edges, we find it sufficient to design prediction schemes which can make effective use of edge information. Working from the intra prediction framework in H.264, we provide a graph representation of pixels in a block and pixels from previously coded blocks and construct an edge-aware prediction scheme based on this. We also employ existing rate-distortion (RD) optimization methods to further improve the coding performance. Our proposed methods reduce the bit rate for depth maps by up to 29% for a fixed interpolated PSNR for some sequences.
Learning Graphs With Monotone Topology Properties and Multiple Connected Components

Recent papers have formulated the problem of learning graphs from data as an inverse covariance estimation problem with graph Laplacian constraints. While such problems are convex, existing methods cannot guarantee that solutions will have specific graph topology properties (e.g., being a tree), which are desirable for some applications. The problem of learning a graph with topology properties is in general non-convex. In this paper, we propose an approach to solve these problems by decomposing them into two sub-problems for which efficient solutions are known. Specifically, a graph topology inference (GTI) step is employed to select a feasible graph topology. Then, a graph weight estimation (GWE) step is performed by solving a generalized graph Laplacian estimation problem, where edges are constrained by the topology found in the GTI step. Our main result is a bound on the error of the GWE step as a function of the error in the GTI step. This error bound indicates that the GTI step should be solved using an algorithm that approximates the data similarity matrix by another matrix whose entries have been thresholded to zero to have the desired type of graph topology. The GTI stage can leverage existing methods, which are typically based on minimizing the total weight of removed edges. Since the GWE stage is an inverse covariance estimation problem with linear constraints, it can be solved using existing convex optimization methods. We demonstrate that our approach can achieve good results for both synthetic and texture image data.
Graph-based transforms for inter predicted video coding

In video coding, motion compensation is an essential tool to obtain residual block signals whose transform coefficients are encoded. This paper proposes novel graph-based transforms (GBTs) for coding inter-predicted residual block signals. Our contribution is twofold: (i) We develop edge adaptive GBTs (EA-GBTs) derived from graphs estimated from residual blocks, and (ii) we design template adaptive GBTs (TA-GBTs) by introducing simplified graph templates generating different set of GBTs with low transform signaling overhead. Our experimental results show that proposed methods significantly outperform traditional DCT and KLT in terms of rate-distortion performance.
Towards a sampling theorem for signals on arbitrary graphs

In this paper, we extend the Nyquist-Shannon theory of sampling to signals defined on arbitrary graphs. Using spectral graph theory, we establish a cut-off frequency for all bandlimited graph signals that can be perfectly reconstructed from samples on a given subset of nodes. The result is analogous to the concept of Nyquist frequency in traditional signal processing. We consider practical ways of computing this cut-off and show that it is an improvement over previous results. We also propose a greedy algorithm to search for the smallest possible sampling set that guarantees unique recovery for a signal of given bandwidth. The efficacy of these results is verified through simple examples.
Video streaming in uplink mode using WiMAX system — experimental results

The paper explores the technical limits and possibilities to transmit live audiovisual contents in the uplink mode by means of IP networks based on WiMAX broadband system using a professional camera. A previous evaluation of the WiMAX link performances in real urban environments is tackled, and then, obtained results used to implement an Automatic Video Coding Rate Adjustment (AVCRA) algorithm that adaptively adjusts the video codec parameters to the experienced wireless channel capacity. The experimental network also contains a designed antenna small enough to be incorporated in the camera. Special considerations regarding the radiation diagram have been taken into account for not affecting the head of the proper cameraman. Obtained experimental WiMAX throughput in function of the signal to noise ratio, and the Peak signal to noise ratio (PSNR) levels demonstrated the feasibility of live video links over WiMAX networks in urban environment.