nils-herrmann/clean_scirepeval huggingface dataset:
Detection and encoding of occluded areas in very low bit rate video coding

One of the challenging problems that most existing video codecs face today is the encoding of the information pertaining to the occluded areas, i.e., the areas which are covered or uncovered by moving objects. The existing techniques necessitate the transmission of the position information of the occluded areas following the detection process, which can constitute a large overhead in bandwidth consumption. In addition, these detection techniques fail under noisy conditions. On the other hand, no effort was made to incorporate the spatio-temporal correlation that exists between motion fields of consecutive frames. A new method to detect the occluded areas is described. In this method, the decoder is given additional intelligence to extract the position information of the occluded areas, thus reducing the bandwidth needed to transmit the occlusion information significantly. According to the proposed method, the temporal correlation of the motion fields is exploited. The proposed method is robust under noisy conditions and provides a computationally simple solution.
General choice of the regularization functional in regularized image restoration

The determination of the regularization parameter is an important issue in regularized image restoration, since it controls the trade-off between fidelity to the data and smoothness of the solution. A number of approaches have been developed in determining this parameter. In this paper, a new paradigm is adopted, according to which the required prior information is extracted from the available data at the previous iteration step, i.e., the partially restored image at each step. We propose the use of a regularization functional instead of a constant regularization parameter. The properties such a regularization functional should satisfy are investigated, and two specific forms of it are proposed. An iterative algorithm is proposed for obtaining a restored image. The regularization functional is defined in terms of the restored image at each iteration step, therefore allowing for the simultaneous determination of its value and the restoration of the degraded image. Both proposed iteration adaptive regularization functionals are shown to result in a smoothing functional with a global minimum, so that its iterative optimization does not depend on the initial conditions. The convergence of the algorithm is established and experimental results are shown.
A recursive nonstationary MAP displacement vector field estimation algorithm

A recursive model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from successive image frames of an image sequence is presented. To model the DVF, we develop a nonstationary vector field model called the vector coupled Gauss-Markov (VCGM) model. The VCGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line process, which governs the transitions between the submodels. A detailed line process is proposed. The VCGM model is well suited for estimating the DVF since the resulting estimates preserve the boundaries between the differently moving areas in an image sequence. A Kalman type estimator results, followed by a decision criterion for choosing the appropriate line process. Several experiments demonstrate the superior performance of the proposed algorithm with respect to prediction error, interpolation error, and robustness to noise.
A novel cost-distortion optimization framework for video streaming over differentiated services networks

This paper presents a novel framework for streaming video over a Differentiated Services (DiffServ) network that jointly considers video source coding, packet classification and error concealment within the scope of cost-distortion optimization. Our formulation incorporates the random network delay for each packet into the calculation of the probability of packet loss and manages the end-to-end packet delay by selecting the encoding parameters and packet priority. We formulate two approaches to evaluate the performance of the proposed framework: a minimum distortion approach and a minimum cost approach, in which the encoding mode and priority class for each packet are optimally selected so as to minimize the total distortion subject to cost constraints, or to minimize the total cost subject to end-to-end distortion constraints. Simulation results demonstrate the advantage of jointly adapting the source coding and packet classification.
A cross-layer approach for energy efficient transmission of progressively coded images over wireless channels

Mobility, made available by today's communication networks, imposes several limitations in the design of multimedia applications, due to high error rates, reduced bandwidth, strong variability, and mobile terminals lifetime. This paper investigates the use of an energy constrained cross-layer approach for the transmission of progressively coded images over packet-based wireless channels. We propose an optimum power allocation algorithm to enable unequal error protection of a pre-encoded image. Simulations are performed modeling transmission over a Rayleigh fading channel. The investigation focuses on JPEG2000, but it is applicable to other progressively coded bitstreams as well. Our experimental results demonstrate that it is possible to achieve a relevant performance enhancement with the proposed approach over uniform error protection. The optimal solution can also serve as a guideline for developing less computationally intensive empiric approaches.
A mixed norm image restoration

In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.
An adaptive coding scheme using affine motion model for MPEG P-VOP

Block matching has been used for motion estimation and motion compensation in MPEG standards for years. While it has an acceptable performance in describing motion between frames, it requires quite a few bits to represent the motion vectors. In certain circumstances, the use of whole frame affine motion models would perform equally well or even better than block matching in terms of motion accuracy, while it results in the coding of only 6 parameters. In this paper, we modify an MPEG-4 codec by adding: (1) 6 affine model parameters to the frame header; and (2) mode selection among INTRA, SKIP, INTER-16/spl times/16, INTER-8/spl times/8, and GLOBAL-AFFINE modes by Lagrange optimal rate-distortion criteria. Simulation results demonstrate 10-20% decrease in bit-rate, compared to the MMS codec for an average coded P-frame with the same reconstruction PSNR.
Image restoration using the Hopfield network with nonzero autoconnection

A modified Hopfield network model for image restoration is presented. The proposed neural network does not require zero autoconnections, which is one of the major drawbacks of the Hopfield network. A new number-representation scheme for implementing the proposed network is given. The proposed network with sequential update is shown to converge. The sufficient conditions for convergence of n-simultaneous updates are also given. When the image-restoration problem does not satisfy the convergence conditions, a greedy algorithm which guarantees convergence (at the expense of the image quality) is used.<<ETX>>
Object-based video compression scheme with optimal bit allocation among shape, motion and texture

In object-based video, 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 scalability. However, the problem of how to jointly encode these separate signals to reach the best coding efficiency has never been solved thoroughly. In this paper, we present an operational rate-distortion optimal bit allocation scheme that provides a solution to this problem. Our approach is based on the Lagrangian relaxation and dynamic programming. Experimental results indicate that the proposed optimal encoding approach has considerable gains over an ad-hoc method without optimization. Furthermore the proposed algorithm is much more efficient than exhaustive search.
Nonstationary iterative image restoration

This paper introduces different types of nonstationary constrained iterative image restoration algorithms. The adaptivity of the algorithm is introduced by the constraint operator which incorporates properties of the response of the human visual system. The properties of the visual system are represented by noise masking and visibility functions. A new way of computing the masking function is also introduced. The proposed algorithms are general and can be used for any type of linear constraint and distortion operators. The algorithms can also be used to restore signals different than images, since the constraint operator can be interpreted as adapting to the local signal activity.
Variance-aware distortion estimation for wireless video communications

The problem of encoding and transmitting a video sequence over a wireless channel is considered. Our objective is to minimize the end-to-end distortion while using a limited amount of transmission energy and delay. In our approach, we jointly adapt the source-coding parameters and transmission power per packet. We introduce the concept of "variance-aware distortion estimation" (VADE), and present a framework for controlling both the expected value and the variance of the end-to-end distortion. This framework is based on knowledge of how the video is compressed, the probability of packet loss, and the concealment strategy. To the best of our knowledge, this paper is the first to address the trade-off between the mean and variance of the end-to-end distortion. Experimental results demonstrate the potential of the proposed approach.
Geospatial image mining for nuclear proliferation detection: Challenges and new opportunities

With increasing understanding and availability of nuclear technologies, and increasing persuasion of nuclear technologies by several new countries, it is increasingly becoming important to monitor the nuclear proliferation activities. There is a great need for developing technologies to automatically or semi-automatically detect nuclear proliferation activities using remote sensing. Images acquired from earth observation satellites is an important source of information in detecting proliferation activities. High-resolution remote sensing images are highly useful in verifying the correctness, as well as completeness of any nuclear program. DOE national laboratories are interested in detecting nuclear proliferation by developing advanced geospatial image mining algorithms. In this paper we describe the current understanding of geospatial image mining techniques and enumerate key gaps and identify future research needs in the context of nuclear proliferation.
A video compression scheme with 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 optimally the bit allocation between the rate and the distortion. This algorithm is based on Lagrangian relaxation and the DP approach introduced for the lossless MCVC. We then present an H.263-based MCVC which uses the proposed optimal bit allocation scheme and compare its results to H.263. As expected, the proposed coder is superior in the rate-distortion sense.
Real-time super-resolution for digital zooming using finite kernel-based edge orientation estimation and truncated image restoration

This paper presents a novel real-time super-resolution (SR) method using directionally adaptive image interpolation and image restoration. The proposed interpolation method estimates the edge orientation using steerable filters and performs edge refinement along the estimated edge orientation. Bi-linear and bi-cubic interpolation filters are then selectively used according to the estimated edge orientation for reducing jagging artifacts in slanting edge regions. The proposed restoration method can effectively remove image degradation caused by interpolation using the directionally adaptive truncated constrained least-squares (TCLS) filter. The proposed method provides high-quality magnified images which are similar to or better than the result of advanced interpolation or SR methods without high computational load. Experimental results indicate that the proposed system gives higher peak-to-peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values than the state-of-the-art image interpolation methods.
Joint object-based video encoding and power management for energy efficient wireless video communications

In this paper, we consider dynamic resource allocation for object-based wireless video communications. In object-based video coding, a video frame is comprised of objects that are described by their shape as well as their texture. By jointly considering source coding, error concealment and transmission power management at the physical layer, the proposed framework minimize the expect distortion at the receiver for given energy and delay constraints. In order to provide unequal error protection for the shape and texture information, a new video packetization scheme is proposed. Experimental results indicate that the proposed unequal error protection schemes significantly outperform equal error protection methods.
Least squares restoration of multichannel images

Multichannel restoration using both within- and between-channel deterministic information is considered. A multichannel image is a set of image planes that exhibit cross-plane similarity. Existing optimal restoration filters for single-plane images yield suboptimal results when applied to multichannel images, since between-channel information is not utilized. Multichannel least squares restoration filters are developed using the set theoretic and the constrained optimization approaches. A geometric interpretation of the estimates of both filters is given. Color images (three-channel imagery with red, green, and blue components) are considered. Constraints that capture the within- and between-channel properties of color images are developed. Issues associated with the computation of the two estimates are addressed. A spatially adaptive, multichannel least squares filter that utilizes local within- and between-channel image properties is proposed. Experiments using color images are described.
A jointly optimal fractal/DCT compression scheme

In this paper a hybrid fractal and discrete cosine transform (DCT) coder is developed. Drawing on the ability of DCT to remove inter-pixel redundancies and on the ability of fractal transforms to capitalize on long-range correlations within the image, the hybrid coder performs an operationally optimal, in the rate-distortion sense, bit allocation among coding parameters. An orthogonal basis framework is used within which an image segmentation and a hybrid block-based transform are selected jointly. The selection of coefficients in the DCT component of the overall block transform is made a part of the optimization procedure. A Lagrangian multiplier approach is used to optimize the hybrid transform parameters together with the segmentation. Differential encoding of the DC coefficient is employed, with the scanning path based on a 3rd-order Hilbert curve. Simulation results show a significant improvement in quality with respect to the JPEG standard, an approach based on optimization of DCT basis vectors, as well as, the purely fractal techniques.
MPEG-4 and rate-distortion-based shape-coding techniques

We address the problem of the efficient encoding of object boundaries. This problem is becoming increasingly important in applications such as content-based storage and retrieval, studio and television postproduction, and mobile multimedia applications. The MPEG-4 visual standard will allow the transmission of arbitrarily shaped video objects. The techniques developed for shape coding within the MPEG-4 standardization effort are described and compared first. A framework for the representation of shapes using their contours is presented next. Such representations are achieved using curves of various orders, and they are optimal in the rate-distortion sense. Finally, conclusions are drawn.
Transmission energy minimization in wireless video streaming applications

Transmitter energy is a valuable resource in wireless networks. Transmitter power management can have an impact on battery life for mobile users, link level QoS and network capacity. We consider efficient use of transmitter energy in a streaming application.. We formulate an optimization problem that corresponds to minimising the transmission energy required to achieve an acceptable level of distortion subject to a delay constraint. By considering jointly the selection of coding parameters and transmitter power, we can formulate an optimal policy. We present results illustrating the advantages of jointly considering these two variables.
Application-Centric Routing for Video Streaming over Multi-hop Wireless Networks

Most existing works on routing for video transmission over multihop wireless networks only focus on how to satisfy the network-oriented quality-of-service (QoS), such as through put, delay, and packet loss rate rather than application-oriented QoS such as the user-perceived video quality. Although there are some research efforts which use application-centric video quality as the routing metric, they either calculate the video quality based on some predefined rate-distortion function or model without considering the impact of video coding and decoding (including error concealment) on routing, or use exhaustive search or heuristic methods to find the optimal path, leading to high computational complexity and/or suboptimal solutions. In this paper, we propose an application-centric routing framework for real-time video transmission in multihop wireless networks, where expected video distortion is adopted as the routing metric. The major contributions of this paper are: 1) the development of an efficient routing algorithm with the routing metric expressed in terms of the expected video distortion and being calculated on-the-fly, and 2) the development of a quality-driven cross-layer optimization framework to enhance the flexibility and robustness of routing by the joint optimization of routing path selection and video coding, thereby maximizing the user-perceived video quality under a given video playback delay constraint. Both theoretical and experimental results demonstrate that the proposed quality-driven application-centric routing approach can achieve a superior performance over existing network-centric routing approaches.
An iterative mixed norm image restoration algorithm

In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.
Efficient frame vector selection based on ordered sets

The problem of finding the optimal set of quantized coefficients for a frame-based encoded signal is known to be of very high complexity. This paper presents an efficient method of finding the operational rate-distortion (RD) optimal set of coefficients. The major complexity reduction lies in the reformulation of the original RD-tradeoff problem, where a new set of coefficients is used as decision variables. These coefficients are connected to the orthogonalization of the set of selected frame vectors and not to the frame vectors themselves. By organizing all possible solutions as nodes in a solution tree, we use complexity saving techniques to find the optimal solution in an even more efficient way. Using an ordered vector selection process, the complexity can be again significantly reduced and efficient run-length encoding becomes feasible. Contrary to the original problem, the new problem can be solved optimally in a reasonable amount of time.
A novel iterative image restoration algorithm using nonstationary image priors

In this paper, we propose a novel algorithm for image restoration based on combining nonstationary edge-preserving priors. We develop a Bayesian modeling followed by an evidence analysis inference approach for deriving the foundations of the proposed iterative restoration algorithm. Simulation results over a variety of blurred and noisy standard test images indicate that the presented method outperforms current state-of-the-art image restoration algorithms. We finally present experimental results by digitally refocusing images captured with controlled defocus, successfully confirming the ability of the proposed restoration algorithm in recovering extra features and details, while still preserving edges.
Iterative determination of local bound constraints in iterative image restoration

In this paper, the problem of how to better estimate spatially adaptive intensity bounds for image restoration is addressed. When the intensity bounds are estimated from a degraded image, blurring leads to underestimation of the bounds in the edge and texture regions. Therefore, an iterative implementation of the restoration algorithm has been proposed in which the intensity bounds are re-estimated from the current image estimate. However, direct update of the bounds leads to over-smoothing in regions where the bounds are active. Furthermore, the resulting algorithm exhibits slow convergence. In this paper, alternative methods of initially estimating and updating the bounds are proposed, and the results for the fixed- and updated-bound implementations are compared. A method for estimation of the bound tightness parameter is also proposed.
Analysis and FPGA Implementation of Image Restoration under Resource Constraints

Programmable logic is emerging as an attractive solution for many digital signal processing applications. In this work, we have investigated issues arising due to the resource constraints of FPGA-based systems. Using an iterative image restoration algorithm as an example we have shown how to manipulate the original algorithm to suit it to an FPGA implementation. Consequences of such manipulations have been estimated, such as loss of quality in the output image. We also present performance results from an actual implementation on a Xilinx FPGA. Our experiments demonstrate that, for different criteria, such as result quality or speed, the best implementation is different as well.