nils-herrmann/clean_scirepeval huggingface dataset:
Joint source-channel coding for wireless object-based video communications utilizing data hiding

In recent years, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a data hiding scheme that improves the error resilience of object-based video by adaptively embedding the shape and motion information into the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on knowledge of the transmission channel conditions. Our goal is to achieve the best video quality as expressed by the minimum total expected distortion. The optimization problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a Rayleigh-fading wireless channel, and the algorithm is implemented based on the MPEG-4 verification model. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection
Application-Aware Approach to Compression and Transmission of H.264 Encoded Video for Automated and Centralized Transportation Surveillance

In this paper, we present a transportation video coding and wireless transmission system specifically tailored to automated vehicle tracking applications. By taking into account the video characteristics and the lossy nature of the wireless channels, we propose video preprocessing and error control approaches to enhance tracking performance while conserving bandwidth resources and computational power at the transmitter. Compared with current state-of-the-art H.264-based implementations, our system is shown to yield over 80% bitrate savings for comparable tracking accuracy.
Jointly optimal coding of texture and shape

A major problem in object oriented video coding and MPEG-4 is the encoding of object boundaries. Traditionally, and within MPEG-4, the encoding of shape and texture information are separate steps (the extraction of shape is not considered by the standards). We present a vertex-based shape coding method which is optimal in the operational rate-distortion sense and takes into account the texture information of the video frames. This is accomplished by utilizing a variable-width tolerance band which is proportional to the degree of trust in the accuracy of the shape information at that location. Thus, in areas where the confidence in the estimation of the boundary is not high and/or coding errors in the boundary will not affect the application (object oriented coding, MPEG-4, etc.) significantly, a larger boundary approximation error is allowed. We present experimental results which demonstrate the effectiveness of the proposed algorithm.
Rate-distortion optimal bit allocation for object-based video coding

In object-based video encoding, the encoding of the video data is decoupled into the encoding of shape, motion, and texture information, which enables certain functionalities, like content-based interactivity and content-based scalability. The fundamental problem, however, of how to jointly encode this separate information to reach the best coding efficiency has not been studied thoroughly. In this paper, we present an operational rate-distortion optimal scheme for the allocation of bits among shape, motion, and texture in object-based video encoding. Our approach is based on Lagrangian relaxation and dynamic programming. We implement our algorithm on the MPEG-4 video verification model, although it is applicable to any object-based video encoding scheme. The performance is accessed utilizing a proposed metric that jointly captures the distortion due to the encoding of the shape and texture. Experimental results demonstrate that the gains of lossy shape encoding depend on the percentage the shape bits occupy out of the total bit budget. This gain may be small or may be realized at very low bit rates for certain typical scenes.
Optimal bit allocation among dependent quantizers for the minimum maximum distortion criterion

In this paper we introduce an optimal bit allocation scheme for dependent quantizers for the minimum maximum distortion criterion. First we show how minimizing the bit rate for a given maximum distortion can be achieved in a dependent coding framework using dynamic programming (DP). Then we employ an iterative algorithm to minimize the maximum distortion for a given bit rate, which invokes the DP scheme. We prove that it converges to the optimal solution. Finally we present a comparison between the minimum total distortion criterion and the minimum maximum distortion criterion for the encoding of an H.263 Intra frame. In this comparison we also point out the similarities between the proposed minimum maximum distortion approach and the Lagrangian multiplier based minimum total distortion approach.
Variational Bayesian Image Restoration With a Product of Spatially Weighted Total Variation Image Priors

In this paper, a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local image features than previous TV based priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed restoration algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar algorithms. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms.
Wireless Video Surveillance: A Survey

A wireless video surveillance system consists of three major components: 1) the video capture and preprocessing; 2) the video compression and transmission in wireless sensor networks; and 3) the video analysis at the receiving end. A myriad of research works have been dedicated to this field due to its increasing popularity in surveillance applications. This survey provides a comprehensive overview of existing state-of-the-art technologies developed for wireless video surveillance, based on the in-depth analysis of the requirements and challenges in current systems. Specifically, the physical network infrastructure for video transmission over wireless channel is analyzed. The representative technologies for video capture and preliminary vision tasks are summarized. For video compression and transmission over the wireless networks, the ultimate goal is to maximize the received video quality under the resource limitation. This is also the main focus of this survey. We classify different schemes into categories including unequal error protection, error resilience, scalable video coding, distributed video coding, and cross-layer control. Cross-layer control proves to be a desirable measure for system-level optimal resource allocation. At the receiver's end, the received video is further processed for higher-level vision tasks, and the security and privacy issues in surveillance applications are also discussed.
Rate distortion optimal ECG signal compression

Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for addressing the problem. In this paper we present a time domain algorithm based on the coding of line segments which are used to approximate the signal. These segments are fit in a way that is optimal in the rate distortion sense. Although the approach is applicable to any type of signal, we focus, in this paper, on the compression of ElectroCardioGram (EGG) signals. ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated that exact optimization algorithms outperform these heuristic approaches by a wide margin with respect to reconstruction error. By formulating the compression problem as a graph theory problem, known optimization theory can be applied in order to yield optimal compression. In this paper we present an algorithm that will guarantee the smallest possible distortion among all methods applying linear interpolation given an upper bound on the number of bits. Compared to many other compression methods, we report superior performance for this method.
Iterative maximum likelihood displacement field estimation in quantum-limited image sequences

We develop an algorithm for obtaining the maximum likelihood (ML) estimate of the displacement vector field (DVP) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, stereo matching, and frame registration in low-light level image sequences as well as low-dose clinical X-ray image sequences. In the latter case, a controlled X-ray dosage reduction may be utilized to lower the radiation exposure to the patient and the medical staff. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. A Fisher-Bayesian formulation is used to estimate the DVF and a block component search algorithm is employed in obtaining the solution. Several experiments involving a phantom sequence and a teleconferencing image sequence with realistic motion demonstrate the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions (20-25 events/pixel).
Image restoration using a modified Hopfield network

A modified Hopfield neural network model for regularized image restoration is presented. The proposed network allows negative autoconnections for each neuron. A set of algorithms using the proposed neural network model is presented, with various updating modes: sequential updates; n-simultaneous updates; and partially asynchronous updates. The sequential algorithm is shown to converge to a local minimum of the energy function after a finite number of iterations. Since an algorithm which updates all n neurons simultaneously is not guaranteed to converge, a modified algorithm is presented, which is called a greedy algorithm. Although the greedy algorithm is not guaranteed to converge to a local minimum, the l (1) norm of the residual at a fixed point is bounded. A partially asynchronous algorithm is presented, which allows a neuron to have a bounded time delay to communicate with other neurons. Such an algorithm can eliminate the synchronization overhead of synchronous algorithms.
High-resolution images from low-resolution compressed video

Surveys the field of super resolution (SR) processing for compressed video. The introduction of motion vectors, compression noise, and additional redundancies within the image sequence makes this problem fertile ground for novel processing methods. In conducting this survey, though, we develop and present all techniques within the Bayesian framework. This adds consistency to the presentation and facilitates comparison between the different methods. The article is organized as follows. We define the acquisition system utilized by the surveyed procedures. Then we formulate the HR problem within the Bayesian framework and survey models for the acquisition and compression systems. This requires consideration of both the motion vectors and transform coefficients within the compressed bit stream. We survey models for the original HR image intensities and displacement values. We discuss solutions for the SR problem and provide examples of several approaches.
Simultaneous optimal boundary encoding and variable-length code selection

This paper describes efficient and optimal encoding and representation of object contours. Contours are approximated by connected second-order spline segments, each defined by three consecutive control points. The placement of the control points is done optimally in the rate-distortion (RD) sense and jointly with their entropy encoding. We utilize a differential scheme for the rate and an additive area-based metric for the distortion to formulate the problem as a Lagrangian minimization. We investigate the sensitivity of the resulting operational RD curve on the variable length codes used and propose an iterative procedure arriving at the entropy representation of the original boundary for any given rate-distortion tradeoff.
DNA Hybridization as a Similarity Criterion for Querying Digital Signals Stored in DNA Databases

We demonstrate via simulation that hybridization of DNA molecules can be used as a similarity criterion for retrieving digital signals encoded and stored in a synthesized DNA database. After introducing some necessary DNA terminology, we briefly explain how digital signals are transformed to DNA sequences. Since retrieval is achieved through hybridization of query and data carrying DNA molecules, we present a mathematical model to estimate hybridization efficiency (also known as selectivity annealing). We show that selectivity annealing is inversely proportional to the mean squared error (MSE) of the encoded signal values. In addition, we show that the concentration of the molecules plays the same role as the decision threshold employed in digital signal matching algorithms. Finally, similar to the digital domain, we define a DNA signal-to-noise ratio (SNR) measure to assess the performance of the DNA-based retrieval scheme. Simulations are presented to validate our arguments
Iterative deconvolution using several different distorted versions of an unknown signal

This paper analyses the error behavior of iterative deconvolution algorithms when the distorting system has a frequency response that has negative real part or has a finite number of isolated zeros. The existence of these zeros at a finite number of discrete frequencies results in an inability of the deconvolution algorithm to restore the lost information at these frequencies with a small number of iterations. A new algorithm is suggested that incorporates multiple distorted versions of the signal and results in a restoration error that approaches zero with a small number of iterations.
Compressive sensing-based image denoising using adaptive multiple sampling and optimal error tolerance

In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method.
A Theory for the Optimal Bit Allocation Between Displacement Vector Field and Displaced Frame Difference

We address the fundamental problem of optimally splitting a video sequence into two sources of information, the displaced frame difference (DFD) and the displacement vector field (DVF). We first consider the case of a lossless motion-compensated video coder (MCVC), and derive a general dynamic programming (DP) formulation which results in an optimal tradeoff between the DVF and the DFD. We then consider the more important case of a lossy MCVC, and present an algorithm which solves the tradeoff between the rate and the distortion. This algorithm is based on the Lagrange multiplier method and the DP approach introduced for the lossless MCVC. We then present an H.263-based MCVC which uses the proposed optimal bit allocation, and compare its results to H.263. As expected, the proposed coder is superior in the rate-distortion sense. In addition to this, it offers many advantages for a rate control scheme. The presented theory can be applied to build new optimal coders, and to analyze the heuristics employed in existing coders. In fact, whenever one changes an existing coder, the proposed theory can be used to evaluate how the change affects its performance.
Shape error concealment using Hermite splines

The introduction of video objects (VOs) is one of the innovations of MPEG-4. The /spl alpha/-plane of a VO defines its shape at a given instance in time and hence determines the boundary of its texture. In packet-based networks, shape, motion, and texture are subject to loss. While there has been considerable attention paid to the concealment of texture and motion errors, little has been done in the field of shape error concealment. In this paper, we propose a post-processing shape error-concealment technique that uses geometric boundary information of the received /spl alpha/-plane. Second-order Hermite splines are used to model the received boundary in the neighboring blocks, while third order Hermite splines are used to model the missing boundary. The velocities of these splines are matched at the boundary point closest to the missing block. There exists the possibility of multiple concealing splines per group of lost boundary parts. Therefore, we draw every concealment spline combination that does not self-intersect and keep all possible results until the end. At the end, we select the concealment solution that results in one closed boundary. Experimental results demonstrating the performance of the proposed method and comparisons with prior proposed methods are presented.
Multichannel restoration of single channel images using a wavelet decomposition

Multichannel linear filtering is applied to the restoration of single-channel images through the use of a wavelet decomposition. A novel matrix structure for the separable 2-D wavelet transform is presented which allows the transformation of block circulant operators, found in 2-D linear filtering problems, into semiblock circulant operators, which are defined here. These operators are easily treated as block diagonal matrices in the wavelet-frequency domain. An adaptive Wiener filter is implemented in this domain, which uses the cross-correlations between subbands in the decomposition to improve substantially the restoration of noisy-blurred images over that found with single-channel filtering. This improvement is especially evident when the power spectrum of the original image is available.<<ETX>>
Restoration of low bit rate compressed images using mean field annealing

Most of the transform-based image compression techniques produce visible artifacts in the reconstructed image, particularly at low bit rates. In this paper, we propose an iterative technique to reduce the unwanted degradations such as blocking artifacts while keeping the necessary detail present in the original image. The proposed technique makes use of a priori information about the original image by using a nonstationary Gauss-Markov model. A MAP estimate is obtained iteratively using mean field annealing. A additional a priori information about the transform of the original image is incorporated into the estimation process by projecting the image onto a set defined by the quantizer at each iteration. The performance of the proposed algorithm was tested on JPEG compressed images. It is shown to be effective in removing the coding artifacts present in the low bit rate compressed images.<<ETX>>
How can DNA computing be applied to digital signal processing?

Although digital signals have been used as inputs in some DNA computing applications, there has been a small research regarding the application of DNA computing principles in solving DSP problems. This article offers a first step towards filling this gap and thus strengthening the ties between biology and signal processing. By focusing the attention of the article to a specific domain, the author believes that many new exciting applications of DNA computation can be discovered. A short overview of molecular biology and tools commonly used in DNA computing is also provided for presentation purposes. This article offers the signal processing community some future directions regarding the unexplored area of research in biocomputing technology.
Variational Gaussian process for missing label crowdsourcing classification problems

In this paper we address the crowdsourcing problem, where a classifier must be trained without knowing the real labels. For each sample, labels (which may not be the same) are provided by different annotators (usually with different degrees of expertise). The problem is formulated using Bayesian modeling, and considers scenarios where each annotator may label a subset of the training set samples only. Although Bayesian approaches have been previously proposed in the literature, we introduce Variational Bayes inference to develop an iterative algorithm where all latent variables are automatically estimated. In the experimental section the proposed model is evaluated and compared with other state-of-the-art methods on two real datasets.
Cost-distortion optimized unequal error protection for object-based video communications

Object-based video coding is a relatively new technique to meet the fast growing demand for interactive multimedia applications. Compared with conventional frame-based video coding, it consists of two types of source data: shape information and texture information. Recently, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a cost-distortion optimal unequal error protection (UEP) scheme for object-based video communications. Our goal is to achieve the best video quality (minimum total expected distortion) with constraints on transmission cost and delay in a lossy network environment. The problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a narrow-band block-fading wireless channel with additive white Gaussian noise and a simplified differentiated services Internet channel. Experimental results indicate that the proposed UEP scheme can significantly outperform equal error protection methods.
Robust network-adaptive object-based video encoding

The joint source-network encoding of object-based video is a very important and challenging research topic, which has not been adequately explored. In this paper, we propose a robust network-adaptive encoding approach for object-based video. The framework jointly considers source coding, packet loss during transmission, and error concealment at the decoding. The proposed method guarantees the minimum expected distortion for the decoded video, by optimally allocating the shape and texture coding parameters at the encoder. The resulting optimization problem is solved by Lagrangian relaxation and dynamic programming. Experimental results demonstrate that the proposed method has significant gains over the non network-adaptive method.
From Global to Local Bayesian Parameter Estimation in Image Restoration using Variational Distribution Approximations

In this paper we present a new Bayesian methodology for the restoration of blurred and noisy images. Bayesian methods rely on image priors that encapsulate prior image knowledge and avoid the ill-posedness of the image restoration problems. Some of these priors depend on global variance parameters, unable to account for local characteristics. Here we first use variational methods to approximate probability posterior distributions for the global model to later use those distributions to define local and more realistic image models which lead to better restored images as it is shown in the experimental section.