nils-herrmann/clean_scirepeval huggingface dataset:
Wavelet estimation of fractional Brownian motion embedded in a noisy environment

This correspondence proposes a wavelet-based fractional Brownian motion (fBm) signal estimation scheme. Despite the fact that wavelet transform approximately whitens the fBm processes, it is observed that statistical dependencies still exist across adjacent wavelet scales and between neighboring wavelet coefficients. These dependencies can be exploited to improve the estimation of fBm signals embedded into noise. The idea is to reorganize the wavelet coefficients into a scale-time mixture model and then carry out the minimum mean-square-error estimation (MMSE) using the model. Experiments show that the proposed scheme obtains better estimates than Wornell and Oppenheim's algorithm, in which the wavelet dependencies are not utilized.
Length-constrained MAP decoding of variable-length encoded Markov sequences

In this paper, we consider the problem of length-constrained maximum a posteriori decoding of a Markov sequence that is variable-length encoded and transmitted over a binary symmetric channel. We convert this problem into one of a maximum-weight k-link path in a weighted directed acyclic graph. The induced graph-optimization problem can be solved by a fast parameterized search algorithm that finds either the optimal solution with high probability, or a good approximate solution otherwise. The proposed algorithm has lower complexity and superior performance than the previous approximation algorithms
Optimal multiresolution quantization for scalable multimedia coding

We have investigated the problem of designing an optimal entropy-constrained multiresolution scalar quantizer under the criterion of minimizing the expected distortion weighted by the probability of transmission rate and for arbitrary probability mass function of signal amplitude. An O(LN/sup 3/) algorithm is proposed to solve the optimization problem, where L is the number of refinement stages, and N is the size of input symbol alphabet. The proposed algorithm is globally optimal, and furthermore, it is more general than its locally optimal predecessors in terms of quantizer structures.
On display-camera synchronization for visible light communication

Recently, Wu and Shu proposed an intriguing technique that transmits data from regular information display devices to smart phone cameras without using any additional hardware or interfering the original content intended to display. This preliminary work reveals the possibility of conducting high-speed data transmission with displays in transparency to human eyes; however, in practice, it could introduce visible artifacts and the display-camera synchronization remains an issue that affects data throughput. In this paper, we examine this technique in more realistic application scenarios and put forward a practical solution that hides the data more effectively and greatly improves the robustness of the transmission. Experimental results show that, using the proposed solution, viewers can hardly notice any artifacts caused by the data embedded in videos, and the bit error rate is acceptable under common use conditions.
Context-based lossless interband compression-extending CALIC

This paper proposes an interband version of CALIC (context-based, adaptive, lossless image codec) which represents one of the best performing, practical and general purpose lossless image coding techniques known today. Interband coding techniques are needed for effective compression of multispectral images like color images and remotely sensed images. It is demonstrated that CALIC's techniques of context modeling of DPCM errors lend themselves easily to modeling of higher-order interband correlations that cannot be exploited by simple interband linear predictors alone. The proposed interband CALIC exploits both interband and intraband statistical redundancies, and obtains significant compression gains over its intrahand counterpart. On some types of multispectral images, interband CALIC can lead to a reduction in bit rate of more than 20% as compared to intraband CALIC. Interband CALIC only incurs a modest increase in computational cost as compared to intraband CALIC.
Multi-dimensional average-interpolating refinement on arbitrary lattices

Multi-dimensional datasets containing local averages of a function arise in many applications such as processing of CCD captures and medical images. Motivated by this fact we introduce multi-dimensional average-interpolating refinement on arbitrary lattices in arbitrary dimensions. Our refinement algorithm results in smooth scaling functions of compact support. This method forms a basis for multi-dimensional multi-resolution analysis and subdivision on datasets obtained by locally averaging a smooth function. As an example, we present two-dimensional polynomial average-interpolating subdivision on the quincunx lattice and show that the resulting scaling functions are highly regular in the sense of Sobolev.
On steganalysis of random LSB embedding in continuous-tone images

We present an LSB steganalysis technique that can detect the existence of hidden messages that are randomly embedded in the least significant bits of natural continuous-tone images. The technique is inspired by the recent work of J. Fridrich et al.  2001) and just like their work, it can also precisely measure the length of the embedded message, even when the hidden message is very short relative to the image size. The key to our success is the formation of some subsets of pixels whose cardinalities change with LSB embedding, and such changes can be precisely quantified under the assumption that the embedded bits are randomly scattered. Interestingly, our study on steganalysis of LSB embedding sheds light on the work of Fridrich et al. on the detection of LSB embedding, and offers an analytical proof of an observation made by them.
Lossy source coding for non-uniform sources using LDGM codes

Recently low-density generator matrix (LDGM) codes along with various message-passing algorithms (e.g., survey propagation algorithm) have been used for lossy source coding. In contrast with most prior work which has focused exclusively on uniformly distributed sources, we address the problem of lossy coding for sources with arbitrary distributions. Built upon the idea of approximating the optimal output distribution indicated by the rate-distortion theory with a uniform distribution over a larger alphabet, two multilevel coding schemes are proposed: one uses multiple linear codes while the other requires a single linear code of extended length. The simulation results show that both schemes can significantly outperform the existing LDGM codes based methods.
Optimal packetization of VLC and convolution coded Markov sequences

We consider the problem of packetizing a variable length coded Markov sequence into fixed length packets, while being protected by variable rate channel code. Given the total transmission bit budget, a joint source-channel coding problem is how to partition the input sequence and how to determine the coding rates of individual packets for minimum expected distortion when the sequence is sent over binary symmetric channel. Three methods are proposed to estimate the performance of a sequence when transmitted through the system, based on which we convert the joint source-channel coding problem into a shortest path problem in a weighted directed acyclic graph which can be solved by using dynamic programming. Simulation shows that the overall performance of the system can be improved by 10-30% compared with the performance of the fixed rate packetization scheme.
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.
Vector quantizer design by constrained global optimization

Central to vector quantization is the design of optimal code book. The construction of a globally optimal code book has been shown to be NP-complete. However, if the partition halfplanes are restricted to be orthogonal to the principal direction of the training vectors, then the globally optimal K-partition of a set of N D-dimensional data points can be computed in O((N+KM/sup 2/)D) time by dynamic programming, where M is the intensity resolution. This constrained optimization strategy improves the performance of vector quantizer over the classic LBG algorithm and the popular methods of tree-structured recursive greedy bipartition of the training data set.<<ETX>>
On packetization of embedded multimedia bitstreams

We study the problem of packetizing embedded multimedia bitstreams to improve the error resilience of source (compression) codes. This problem is important because of the increasing popularity of embedded compression methodology and its suitability for scalable streaming media over IP or/and mobile IP. We study various packetization schemes against packet erasure at both low and high bit rates. Maximizing packetization efficiency for embedded bitstreams is formulated as a discrete optimization problem and globally optimal packetization (OP) algorithms are proposed under different settings. Suboptimal packetization algorithms are also devised to reduce the complexity of the OP algorithms. In order to assess their effectiveness, the proposed packetization algorithms are used to packetize embedded image and video bitstreams with simulated packet loss. Experimental results show that our OP algorithms slightly outperforms suboptimal ones. In addition to confirming the superiority of the OP algorithms, these results also provide justification of heuristic packetization methods published in the literature.
Globally optimal uneven error-protected packetization of scalable code streams

In this extended abstract we present a family of new algorithms for rate-fidelity optimal packetization of scalable source bit stream with uneven error protection. In the most general setting where no assumption is made on the probability function of packet loss or on the rate-fidelity function of the scalable code stream, one of our algorithms can find the globally optimal solution to the problem in O(N/sup 2/L/sup 2/) time, compared to a previously claimed O(N/sup 3/L/sup 2/) complexity, where N is the number of packets and L is the packet payload size. The time complexity can be reduced to O(NL/sup 2/) if the rate-fidelity function of the input is convex and under the reasonable assumption that the probability function of packet loss is monotonically decreasing. In the convex case the algorithm of Mohr et al. (2000) has complexity O(N/sup 2/L log N). Furthermore, our O(NL/sup 2/) algorithm for the convex case can be modified to find an approximation solution for the general case that is better than the results of other algorithms in the prior literature. All of our algorithms do away with the expediency of fractional redundancy allocation, a limitation of some existing algorithms. To our best knowledge this work offers for the first time globally optimal solutions to the important problem of optimal UEP packetization.
Buffer size reduction through buffer sharing for streaming applications

Many multimedia streaming applications have to buffer a number of different source streams for playback of a single multimedia composition. Multiple buffers (one for each stream) have to be deployed at the decoder to make continuous, almost real-time, playback of the multimedia content possible. We propose a novel implementation of two buffers in one array that efficiently reduces the overall memory dedicated to buffering by allowing for a common space where data for both buffers can be stored. An algorithm for finding optimal parameters of the shared buffer and calculating the reduction in the buffer size by buffer sharing is proposed. This involves finding level sets of solutions to some well studied 2D partial differential equations of mathematical physics with simple boundary conditions.
A hidden Markov model-based methodology for intra-field video deinterlacing

This paper presents a new technique of hidden Markov model (HMM) for video deinterlacing. Existing deinterlacing algorithms estimate missing pixels of an absent row in an interlaced frame on a sample-by-sample basis. In contrast, the proposed HMM-based deinterlacing technique adopts an approach of sequence estimation and makes a joint decision on the row of missing pixels as a whole. This allows a more thorough exploitation of the spatial correlation of the image signal. The HMM-based sequence estimation technique is coupled with a number of existing spatial deinterlacing algorithms in the literature to boost their performance. Experimental results show that HMM can significantly improve the deinterlacing results in both PSNR measure and subjective visual quality.
Order statistics preserving near-lossless image coding

We introduce a new concept of near-lossless image compression called order statistics preserving (OSP) near-lossless coding. Unlike ubiquitous L/sub 2/ (PSNR) and common near-lossless criterion of L/sub /spl infin// the OSP is a context-based fidelity measure that can meet more stringent requirements of high-end users in medical, space, and scientific communities.
A subjective distortion measure for vector quantization

The authors present some preliminary results of their ongoing study on subjective VQ distortion measure in the time/spatial domain. They first propose a context based distortion measure between two vectors. The new measure is intuitively appealing, and they include some empirical evidence for its subjective significance. Although the measure is formulated as a matrix norm, it is computationally no more difficult than the mean-squares error. This measure quantifies the quantization distortion in the context (shape) of the signal waveform, but it is amplitude-invariant. So they combine the context distortion measure with a weighted mean distortion measure to obtain a unified subjective distortion measure D. They show that D is a distance measure and can be easily computed. Moreover, the process of computing the centroid of a set of training vectors and designing the VQ codebook under the new subjective distortion measure D is as simple as the conventional VQ. Specifically, the LBG algorithm can be applied to design the subjective VQ codebook after a simple linear transformation of the vector space in which signal samples are originally taken. They also analytically relate their subjective distortion measure to the ubiquitous mean-squares measure, and demonstrate that the latter is only a special case of the former. They also observe that the mean-removed VQ in a sense clusters training vectors under the proposed context distortion measure.<<ETX>>
LED Backlight Adjustment for Backward-Compatible Stereoscopic Display

It was recently shown that a high-speed optoelectronic display, via a novel signal processing technique called temporal psychovisual modulation (TPVM), can exhibit stereoscopic images to viewers wearing 3-D glasses and clean 2-D images to those without glasses all at the same time. This research aims to improve the above backward-compatible stereoscopy method by adjusting the backlight of today's common LED-lit liquid crystal display systems. Visual quality of the system can be enhanced by jointly optimizing the backlight intensity and the image signal at a negligible extra computational cost. For real-time applications of low-cost consumer electronics, this work also provides a low-complexity solution of backward-compatible stereoscopic display, in which highest 3-D quality is ensured with a small compromise of the 2-D quality.
A model-based approach for human head-and-shoulder segmentation

Object boundary extraction has long been a fundamental research topic, as well as an essential component in many visual computing and communication algorithms, such as computer vision, robotics, pattern recognition and video compression. Under this topic, human head-and-shoulder segmentation is of particular meaning, given the ubiquity of head-and-shoulder type of videos in social media, teleconferencing, and entertainment. Although human visual system can easily detect and recognize the head and upper body of a person, this seemingly simple task still poses a challenge to computers. In this paper, an effective and efficient segmentation method is proposed. This method consists of a novel human body descriptor in polar coordinates and a Markov chain based boundary model, which work together to generate precise boundary results. Moreover, dynamic programming is employed in this work, so as to accelerate the segmentation process. Comparisons with other algorithms are made in the experimental part, which clearly exhibits the advantage of our proposed method over some of its precedents.
Detection of LSB steganography via sample pair analysis

This paper introduces a new, principled approach to detecting least significant bit (LSB) steganography in digital signals such as images and audio. It is shown that the length of hidden messages embedded in the least significant bits of signal samples can be estimated with relatively high precision. The new steganalytic approach is based on some statistical measures of sample pairs that are highly sensitive to LSB embedding operations. The resulting detection algorithm is simple and fast. To evaluate the robustness of the proposed steganalytic approach, bounds on estimation errors are developed. Furthermore, the vulnerability of the new approach to possible attacks is also assessed, and counter measures are suggested.
Down-sampling based embedded compression in video systems

With rapid increase of image resolution in modern video processing and display systems, the bandwidth and power consumption of external memory are becoming serious bottlenecks. This problem can be alleviated by high-fidelity embedded compression (EC) techniques for video frame buffers. Classic lossless or near-lossless coding methods like CALIC are ill suited for embedded systems due to their high complexity. In this work, a new, simple infra-frame EC technique based on downsampling and side-information aided upsampling is developed. Through a study of a family of downsampling schemes, an optimal one is found and analyzed for EC. This downsampling scheme gives birth to the new EC technique. The main idea is to first split an image into blocks, and then adaptively choose different down sampling patterns and upsampling methods to code/decode these blocks. For a memory bandwidth reduction of 60%, the proposed EC system can achieve PSNR above 40dB, while allowing very simple, low-cost real-time hardware realization. A noteworthy novelty of this work is compression without entropy coding. The resulting code stream is of fixed-rate, supporting random access to pixel blocks.
Context-based interpolation of 3-D medical images

A context-based 3D interpolation technique is proposed to enhance the out-of plane resolution of 3D medical images. The proposed technique represents a new approach of aiding 3D interpolation and improving its performance by efficient use of domain knowledge about the anatomy, objects and imaging modalities. In the new approach a family of adaptive 3D interpolation filters are designed and conditioned on different spatial contexts (classes of feature vectors). Training is used to incorporate the domain knowledge into the design of these interpolators. Experimental results show significant improvement of the new approach over some existing 3D interpolation techniques.
Image compression based on multi-scale edge compensation

In this paper we introduce a new image model called MSEC (Multi-Scale Edge Compensation) and implement an image compression system based on MSEC. MSEC consists of semantically meaningful components including edges of different types and scales, and the background. The MSEC-based image coding technique permits some image analysis operations such as edge detection, enhancement, and content-based retrieval to be carried out directly in the compression domain.
Image enhancement revisited: From first order to second order statistics

This paper proposes a new image enhancement algorithm in a recently published framework of optimal contrast-tone mapping (OCTM). The new algorithm represents a fundamental departure from traditional histogram-based image enhancement techniques (i.e., histogram equalization and all of its variants), in that second-order rather than first-order statistics is used. Perceptual quality attributes, such as contrast and tone, are quantified by joint distribution of the values of spatially adjacent pixels instead of histograms as of today. The problem of image enhancement is then formulated as one of linear programming, at the heart of which is a joint distribution-based objective function that can accommodate various psychovisual properties related to image quality. The new linear program algorithm for image enhancement is implemented and its superior performance in visual quality is empirically verified, corroborating with our analysis.
Rate-distortion optimized network communication using general MDC

Efficient delivery scheme for multimedia content is critical for the quality of various multimedia network services. It has been shown that the joint design of the source coding techniques and network communication strategies for multimedia streaming can outperform the traditional separate design in a rate-distortion measure. In this paper, we show that current joint design framework in which a data flow optimization followed by a balanced multiple description coding (MDC) can be further improved by allowing more general MDC techniques. A new general MDC construction algorithm is proposed, and the simulation results show a significant performance improvement over the previous balanced MDC based algorithm.
Contrast enhancement with chromaticity error bound

All existing contrast enhancement methods focus on heightening of spatial details in the luminance channel, with no or little consideration of the color fidelity of the processed images; they can introduce highly noticeable distortions in hue and saturation. This long-time much overlooked problem is addressed by this paper. A new algorithm based on Wu's optimal contrast-tone mapping (OCTM) is proposed to make maximal gain of contrast while keeping the hue component the same and at the same time respecting an upper bound on the distortion of saturation. Experimental results demonstrate the superior perceptual quality of the new chromaticity-conserving contrast enhancement algorithm over existing methods.
Acceleration of the LBG algorithm

A concentric spherical search technique is proposed to speed up the clustering process in VQ design. A linear data structure is incorporated into the LBG algorithm to keep and update the information about the proximity among the codewords. This proximity information can significantly reduce the number of candidate codewords to be the closest to a given training vector. An improved k-means type VQ design algorithm is proposed based on the new search technique and the supporting data structure. The new algorithm is simple to implement, valid for general error metric, and demonstrated by present experiments to be considerably faster than previous algorithms.