nils-herrmann/clean_scirepeval huggingface dataset:
An Efficient and Highly Parallel Hyperspectral Imagery Compression Scheme Based on Distributed Source Coding

This paper extends our previous work on hyperspectral imagery compression based on distributed source coding (DSC). We apply DSC principles to facilitate efficient parallel encoder implementations with moderate memory requirement. Based on our previously proposed wavelet-based DSC framework, we propose a novel adaptive coding scheme that judiciously combines DSC and intra coding tools, taking into account the source statistics and inter-band correlation, as well as the coding gains and limitations imposed by tools. Bits extracted from wavelet coefficients tend to have different statistics and inter-band correlation at different significance levels and in different wavelet subbands. Therefore, it is non-trivial to determine the optimal coding strategy. Toward this we propose modeling techniques to estimate the performance of DSC/intra coding under different bits extraction scenarios. This model is used to define adaptive coding strategies that can optimally incorporate different bit- extraction techniques with DSC/intra coding tools according to the bit significance levels and wavelet subbands. Experimental results demonstrate that adaptive coding can achieve up to 4dB improvement over a non-adaptive system, and the improved DSC-based system is comparable to some 3D wavelet system in terms of coding performance. While we focus on hyperspectral images in this paper, many of the proposed techniques are applicable to other wavelet- based DSC image and video applications.
Accelerated sensor position selection using graph localization operator

This paper addresses the problem of finding optimal sensor placement, i.e., determining F sensor positions from N possible locations. We propose a sensor selection method based on the localization operator of graph signal processing. This method can select sensors while considering the localizations both in graph vertex domain and graph spectral domain and is fast, since eigendecomposition of graph Laplacian matrix is not required. We also propose an interpretation of the conventional node selection based on graph sampling theory by using the graph localization operators. Experiments on selected sensor location, execution time and prediction error comparisons are conducted to show the effectiveness of our approach.
Hyperspectral image coding using graph wavelets

Hyperspectral imaging captures the spectral responses of different wavelengths per pixel for an entire image. Because the number of spectral bands is large, efficient compression of hyperspectral images is important. Leveraging on recent advances in graph signal processing (GSP), in this paper we propose to encode a hyperspectral image in groups of ω spectral bands using graph wavelets, exploiting correlations along both the spatial and the spectral dimensions. Specifically, along the spatial dimension, we estimate the inter-pixel correlations for all adjacent pixel pairs from the last image in the previous coded band group. Along the spectral dimension, we first divide an image into different spatial regions with similar spectral responses, and encode the spectral signature (correlations along the spectrum) for each region as side information (SI). The spatial / spectral correlations are used to compute edge weights to construct a graph for signal-adaptive graph wavelet based compression. Experimental results suggest that our proposal can outperform existing schemes noticeably at comparable complexity.
Estimation of erased data in a H.263 coded stream by using unbalanced multiple description coding

In this work we tackle the problem of error propagation that packet losses can cause in commonly used predictive video coding environments. Using unbalanced multiple description coding (UMDC) to generate redundant source data, we apply the consistency sequence estimation (CSE) algorithm for estimating the lost data. The CSE algorithm, proposed by Singh and Ortega for a 1-D input source signal in a DPCM context, uses a sequence search to verify the consistency of the estimates with the received data. The novelty of our work is the extension of the CSE algorithm to a block matching ME/MC video coder (such as H.263). To reach this aim we propose to work in the spatial domain. Among the advantages of the proposed error resilience scheme are its low-complexity and its compatibility with standard video coders.
Line-based, reduced memory, wavelet image compression

This paper addresses the problem of low memory wavelet image compression. While wavelet or subband coding of images has been shown to be superior to more traditional transform coding techniques, little attention has been paid until recently to the important issue of whether both the wavelet transforms and the subsequent coding can be implemented in low memory without significant loss in performance. We present a complete system to perform low memory wavelet image coding. Our approach is "line-based" in that the images are read line by line and only the minimum required number of lines is kept in memory. There are two main contributions of our work. First, we introduce a line-based approach for the implementation of the wavelet transform, which yields the same results as a "normal" implementation, but where, unlike prior work, we address memory issues arising from the need to synchronize encoder and decoder. Second, we propose a novel context-based encoder which requires no global information and stores only a local set of wavelet coefficients. This low memory coder achieves performance comparable to state of the art coders at a fraction of their memory utilization.
3-D video coding using depth transition data

The objective of this work is to develop a new 3-D video coding system which can provide better coding efficiency with improved subjective quality as compared to existing 3-D video systems. We have analyzed the distortions that occur in rendered views generated using depth image based rendering (DIBR) and classified them in order to evaluate their impact on subjective quality. As a result, we found that depth map coding distortion leads to “erosion artifacts” at object boundaries, which lead to significant degradation in perceptual quality. To solve this problem, we propose a solution in which depth transition data is encoded and transmitted to the decoder. Depth transition data for a given pixel indicates the camera position for which this pixel's depth will change. A main reason to consider transmitting explicitly this information is that it can be used to improve view interpolation at many different intermediate camera positions. Simulation results show that the subjective quality can be significantly improved by reducing the effect of erosion artifacts, using our proposed depth transition data. Maximum PSNR gains of about 0.5 dB can also be observed.
Sampling-Based Correlation Estimation for Distributed Source Coding Under Rate and Complexity Constraints

In many practical distributed source coding (DSC) applications, correlation information has to be estimated at the encoder in order to determine the encoding rate. Coding efficiency depends strongly on the accuracy of this correlation estimation. While error in estimation is inevitable, the impact of estimation error on compression efficiency has not been sufficiently studied for the DSC problem. In this paper, we study correlation estimation subject to rate and complexity constraints, and its impact on coding efficiency in a DSC framework for practical distributed image and video applications. We focus on, in particular, applications where binary correlation models are exploited for Slepian-Wolf coding and sampling techniques are used to estimate the correlation, while extensions to other correlation models would also be briefly discussed. In the first part of this paper, we investigate the compression of binary data. We first propose a model to characterize the relationship between the number of samples used in estimation and the coding rate penalty, in the case of encoding of a single binary source. The model is then extended to scenarios where multiple binary sources are compressed, and based on the model we propose an algorithm to determine the number of samples allocated to different sources so that the overall rate penalty can be minimized, subject to a constraint on the total number of samples. The second part of this paper studies compression of continuous-valued data. We propose a model-based estimation for the particular but important situations where binary bit-planes are extracted from a continuous-valued input source, and each bit-plane is compressed using DSC. The proposed model-based method first estimates the source and correlation noise models using continuous-valued samples, and then uses the models to derive the bit-plane statistics analytically. We also extend the model-based estimation to the cases when bit-planes are extracted based on the significance of the data, similar to those commonly used in wavelet-based applications. Experimental results, including some based on hyperspectral image compression, demonstrate the effectiveness of the proposed algorithms.
Torque ripple reduction in a PMSM driven by direct torque control

Permanent magnet synchronous motors (PMSM) are extensively used in high-performance industrial applications. The electromagnetic torque in a PMSM is proportional to the angle between the stator and rotor flux linkages. Therefore, high dynamic response can be achieved by means of direct torque control (DTC). The main advantage of DTC is its structure simplicity, since no coordinate transformations and no PWM generation are needed. However, high torque ripple is produced when making use of full voltage vectors in classical DTC. The paper presents an improved PMSM DTC scheme by using an active-null vector modulation strategy based on an rms torque-ripple equation minimization. The proposed strategy improves the performances of DTC by combining low torque ripple characteristics in steady state with the faster torque dynamics. Theoretical development and simulation results by the classical and improved DTC are presented and compared to support the research.
Rate-Distortion Analysis and Bit Allocation Strategy for Motion Estimation at the Decoder using Maximum Likelihood Technique in Distributed Video Coding

Numerous approaches for distributed video coding have been recently proposed. One of main motivations for these techniques is the possibility of achieving complexity tradeoffs between the encoder and the decoder that may not be feasible in the context of conventional video coding. In our previous work, a maximum likelihood (ML) method for motion estimation at the decoder was proposed. It was shown that the ML method, designed based on the PRISM architecture, induces no additional rate cost, and is able to work with existing methods, e.g. those based on hash functions or CRC, to improve overall decoding PSNR. In this work, we present a rate-distortion analysis of our ML method. This analysis, given a correlation model for the video data, allows us to improve bit allocation at the encoder, i.e., the decision on the number of cosets to be used to represent various types of video information. We also signal "end of block" (EOB) in coding to further exploit the energy compaction. Our experiments demonstrate significant improvements PSNR up to 1.5 dB from RD optimized bit allocation.
Data-driven operation performance evaluation of multi-chiller system using self-organizing maps

Industrial plants performance evaluation has become a difficult task due to the machinery complexity. Multi-chiller systems take up big proportion of energy in food and beverage companies. Complex refrigeration generation is usually hard to evaluate as the affectation of external signals plays an important role and also exist too many control features for the facility operator. Develop a method able to detect any deviation respect the optimal operation can provide the necessary information for the purpose of inefficiencies identification and a further optimization. In this paper, data-driven methods are used in order to describe a reliable coefficient of performance indicator (COP) in several known plant conditions. Self-organizing maps (SOM) are used to recognize different operating points among the multi-variable feature space for later performance evaluation. By the analysis of COP in each operating point, the potential energy saving can be illustrated. An experimental study is performed with refrigeration plant indicating the suitability of the proposed method.
ICTD Work, Plus mFeel

This issue's Works-In-Progress department has four entries related to the issue's theme, Information and Communication Technologies for Development (ICTD). They are “Sustainable ICT in Agricultural Value Chains”, “Measuring Social Inclusion in Primary Schools”, “An Architecture for Green Mobile Computation”, and “Improving Communication in Resource-Poor Settings”. A fifth entry, “mFeel: An Affective Mobile System”, covers the mFeel mobile system, which combines context awareness with affective and cognitive techniques.
Reduced-Complexity Delayed-Decision Algorithm for Context-Based Image Processing Systems

It is well known that the performance of context-based image processing systems can be improved by allowing the processor (e.g., an encoder or a denoiser) a delay of several samples before making a processing decision. Often, however, for such systems, traditional delayed-decision algorithms can become computationally prohibitive due to the growth in the size of the space of possible solutions. In this paper, we propose a reduced-complexity, one-pass, delayed-decision algorithm that systematically reduces the size of the search space, while also preserving its structure. In particular, we apply the proposed algorithm to two examples of adaptive context-based image processing systems, an image coding system that employs a context-based entropy coder, and a spatially adaptive image-denoising system. For these two types of widely used systems, we show that the proposed delayed-decision search algorithm outperforms instantaneous-decision algorithms with only a small increase in complexity. We also show that the performance of the proposed algorithm is better than that of other, higher complexity, delayed-decision algorithms.
Efficient sensor position selection using graph signal sampling theory

We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the existing methods in the context of graph signal processing and clarify the relationship between these methods and the proposed approach. The effectiveness of our approach is verified through numerical experiments, showing advantages in prediction error and execution time.
Coding structure optimization for interactive multiview streaming in virtual world observation

While most multiview coding techniques focus on compressing all frames in a multiview video sequence in a rate-distortion optimal manner, in this paper we address the problem of interactive multiview streaming, where we minimize the expected transmission rate of an interactive multiview video stream, where the observer can select the view of the next frame, subject to a storage constraint. We show that gains can be achieved by optimizing the trade-off between overall storage and transmission rate, i.e., by storing a more redundant multiview representation (where some frames are encoded more than once, each time using a different reference frame) it is possible to reduce the overall bandwidth needed for online interactive viewing. We show that our proposed redundant representation can reduce the transmission cost of interactive multiview streaming by up to 65% as compared to a good non-redundant representation for the same storage constraint.
Complexity-distortion tradeoffs in vector matching based on probabilistic partial distance techniques

We consider the problem of searching for the best match for an input among a set of vectors, according to some predetermined metric. Examples of this problem include the search for the best match for an input in a VQ encoder and the search for a motion vector in motion estimation-based video coding. We propose an approach that computes a partial distance metric and uses prior probabilistic knowledge of the reliability of the estimate to decide on whether to stop the distance computation. This is achieved with a simple hypothesis testing and the result, an extension of the partial distance technique of Bei and Gray (1985) provides additional computation savings at the cost of a (controllable) loss in matching performance.
Coding techniques for oversampled steerable transforms

In this paper we study signal representation using oversampled steerable transforms. While in general it may not be efficient to use an oversampled representation for applications like compression, our work investigates efficient techniques for representing the oversampled data, given that after oversampling there exists substantial redundancy. We discuss different strategies which take advantage of this oversampling by establishing some consistency constraints on the representation that reduce uncertainty in the quantization. This results in a coding gain as we increase the oversampling in the steerable transform (number of orientations). Thus, while in general it will not be possible to achieve as good compression performance as with a critically sampled transform, having a compressed steerable representation will be useful for applications where a feature is needed (many significant image features can be extracted from an orientation analysis), and where for performance reasons it is preferable not to have to decompress and analyze each image (as may be necessary if standard non-steerable transforms are used for compression).
Multimodal Forecasting Methodology Applied to Industrial Process Monitoring

Industrial process modeling represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, accurate models of critical signals need to be designed in order to forecast process deviations. In this work, a novel multimodal forecasting methodology based on adaptive dynamics packaging and codification of the process operation is proposed. First, a target signal is decomposed by means of the empirical mode decomposition in order to identify the characteristic intrinsic mode functions. Second, such dynamics are packaged depending on their significance and modeling complexity. Third, the operating condition of the considered process, reflected by available auxiliary signals, is codified by means of a self-organizing map and presented to the modeling structure. The forecasting structure is supported by a set of ensemble adaptive-neurofuzzy-inference-system-based models, each one being focused on a different set of signal dynamics. The performance and effectiveness of the proposed method are validated experimentally with industrial data from a copper rod manufacturing plant and performance comparison with classical approaches. The proposed method shows improved performance and generalization over classical single-model approaches.
Stochastic rate-control of interframe video coders for VBR channels

We propose a new algorithm for the real-time control of an inter-frame video coder operating with a variable rate channel such as wireless channels or the Internet. Using techniques of stochastic dynamic programming we obtain off-line optimal policies from stochastic models of the channel and coder which minimize the average expected distortion. The on-line complexity of our approach is only that required to identify the state of the system (source and channel). The state of the channel is obtained based on the ARQ error-control mechanism, and the source state is computed as complexity measurements on each incoming frame. Simulation results based on this new approach are provided and compared to other proposed rate-control strategies. They show how our model-based optimal policies outperform the other considered approaches keeping a negligible on-line computational cost. This result is very interesting when considering an alternative to traditional costly solutions based on deterministic dynamic programming.
Dependent bit allocation in multiview video coding

We consider the bit allocation problem in multiview video coding (MVC). A dependent coding technique using trellis expansion and the Viterbi algorithm (VA) is proposed, which takes into account dependencies across time and views. We note that, typically, optimal quantizer choices have the following properties: i) quantization choices tend to be similar for frames that are consecutive (in time or in view), ii) better quantization tends to be used for frames closer to the root of the dependency tree. We propose a search algorithm to speed up the optimization of quantization choices. Our results indicate significant gains can be achieved by an appropriate selection of bit allocation across frames.
Graph Learning From Data Under Laplacian and Structural Constraints

Graphs are fundamental mathematical structures used in various fields to represent data, signals, and processes. In this paper, we propose a novel framework for learning/estimating graphs from data. The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretations, and (iii) associated algorithms. Specifically, graph learning problems are posed as the estimation of graph Laplacian matrices from some observed data under given structural constraints (e.g., graph connectivity and sparsity level). From a probabilistic perspective, the problems of interest correspond to maximum a posteriori parameter estimation of Gaussian–Markov random field models, whose precision (inverse covariance) is a graph Laplacian matrix. For the proposed graph learning problems, specialized algorithms are developed by incorporating the graph Laplacian and structural constraints. The experimental results demonstrate that the proposed algorithms outperform the current state-of-the-art methods in terms of accuracy and computational efficiency.
Motion compensation based on implicit block segmentation

Block-based motion and disparity compensation are popular techniques to exploit correlation between video frames. Block sizes used for compensation can be chosen to achieve a good trade-off between signaling overhead and prediction accuracy. However, motion field boundaries correspond to objects having arbitrary shapes; this limits the accuracy of block-based compensation, even when small block sizes are chosen. In this paper we seek to enable compensation based on arbitrarily-shaped regions, while preserving an essentially block-based compensation architecture. To do so, we propose tools for implicit block-segmentation and predictor selection. Given two candidate block predictors, segmentation is applied to the difference of predictors. Then a weighted sum of predictors in each segment is selected for prediction. Simulation results show improvements in rate-distortion (R-D) performance, as compared to the standard quad tree approach in H.264/AVC.