nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Algorithms for Optimal Uneven Protection of Single and Multiple Scalable Code Streams Against Packet Erasures

we study algorithmic approaches for rate-fidelity optimal packetization of a single and multiple scalable source code streams with uneven erasure protection (UEP). A new algorithm is developed to obtain the globally optimal solution for scalable source codes of convex rate-fidelity function and for a wide class of erasure channels, including channels for which the probability of losing packets is monotonically nonincreasing in , and independent erasure channels with packet erasure rate smaller than 0.5. This is achieved at linear space complexity and near-linear time complexity in the transmission budget, representing significant improvement over the known globally optimal algorithm. When applied to SPIHT compressed images, the results of the proposed algorithm are virtually the same as the global optima. The above success is also extended to UEP packetization of multiple scalable code streams. We improve the existing algorithms in both speed and performance.
Content Adaptive Embedded Compression

Image resolution in modern video processing and display systems are rocketing up in recent years. With the main stream video quality evolving from standard definition to high-definition, and further towards the emerging super high-definition, the bandwidth and power consumption of external memory are becoming serious bottlenecks. In this paper, frequency domain analysis is made on image down-sampling, which gives birth to the optimal sampling strategy for high-frequency energy protection. On that basis, we develop a new embedded compression (EC) technique, which encodes image frames through content-adaptive down-sampling and decodes them using side-information aided up-sampling. Apart from the low encoding complexity and even lower decoding complexity, the proposed EC algorithm also features high fidelity for images with sharp edges. Comparisons with some existing EC algorithms show the advantage of the new technique over its counterparts on a wide class of testing images.
A new framework of LSB steganalysis of digital media

We propose a general framework for the detection of the least significant bit (LSB) steganography using digital media files as cover objects. The new framework exploits high-order statistics of the samples. It can compute a robust estimate of the length of a secret message hidden in the LSBs of samples for a large class of digital media contents such as image, video, and audio, in which the underlying signals consist of correlated samples. A case study on the LSB steganalysis of natural grey-scale and color images and experimental results are reported.
On initialization of Max's algorithm for optimum quantization

Two methods for initializing J. Max's (1960) iterative algorithm for optimum quantization for a rapid convergence of the algorithm are derived and tested. The methods, which are based on making an intelligent guess of the starting point, perform considerably better than the existing methods for a wide class of reasonably well-behaved unimodal density functions, while the amount of computations involved is negligible compared with what is involved in a single iteration of Max's algorithm. An interesting relationship between optimum quantizers of different levels is revealed in the derivation of these methods. This relationship could also be useful for scalar quantizer design.
Directly operable image representation of multiscale primal sketch

In this paper, we propose a versatile semantics-driven image representation that can support many common operations in visual computing and communications, in addition to being itself an efficient image coding scheme. The proposed image representation is based on a semantically meaningful construct called multiscale primal sketch (MPS). The MPS consists of edges that are extracted and organized successively from fine to coarse scales. The edges are further classified into two types: pulse edge and step edge. MPS is an intermediate-level image representation, which is between pixel-based (low-level) and object-based (high-level) descriptions. The MPS image representation reaches a good compromise between its construction cost and descriptive power. It has a compact form, and hence is amenable to image compression. Furthermore, because the new representation consists of semantically meaningful primitives - edges of different scales and types, and background - many common image operations, such as classification, restoration, detection, and content-based retrieval, can be performed directly in the MPS framework, without first converting the coded image back to the spatial domain.
Multiscale LMMSE-based image denoising with optimal wavelet selection

In this paper, a wavelet-based multiscale linear minimum mean square-error estimation (LMMSE) scheme for image denoising is proposed, and the determination of the optimal wavelet basis with respect to the proposed scheme is also discussed. The overcomplete wavelet expansion (OWE), which is more effective than the orthogonal wavelet transform (OWT) in noise reduction, is used. To explore the strong interscale dependencies of OWE, we combine the pixels at the same spatial location across scales as a vector and apply LMMSE to the vector. Compared with the LMMSE within each scale, the interscale model exploits the dependency information distributed at adjacent scales. The performance of the proposed scheme is dependent on the selection of the wavelet bases. Two criteria, the signal information extraction criterion and the distribution error criterion, are proposed to measure the denoising performance. The optimal wavelet that achieves the best tradeoff between the two criteria can be determined from a library of wavelet bases. To estimate the wavelet coefficient statistics precisely and adaptively, we classify the wavelet coefficients into different clusters by context modeling, which exploits the wavelet intrascale dependency and yields a local discrimination of images. Experiments show that the proposed scheme outperforms some existing denoising methods.
L/sub /spl infin//-constrained high-fidelity image compression via adaptive context modeling

We study high-fidelity image compression with a given tight bound on the maximum error magnitude. We propose some practical adaptive context modeling techniques to correct prediction biases caused by quantizing prediction residues, a problem common to the current DPCM like predictive nearly-lossless image coders. By incorporating the proposed techniques into the nearly-lossless version of CALIC, we were able to increase its PSNR by 1 dB or more and/or reduce its bit rate by ten per cent or more. More encouragingly, at bit rates around 1.25 bpp our method obtained competitive PSNR results against the best wavelet coders, while obtaining much smaller maximum error magnitude.
A segmentation-based predictive multiresolution image coder

Recursive rectilinear tessellations like quadtree are widely used in image coding, but a regular tessellation, despite simple geometry, may not suit image compression because it is too rigid to reflect the scene structure of an image. The paper presents a new image pyramid formed by adaptive, tree-structured segmentation to be a framework of a predictive multiresolution image coder. Subjectively appealing compression results are obtained at different resolutions by scene-adaptive, tree-structured segmentation and by exploiting the statistical dependency between the layers of the image pyramid. The adaptive segmentation-based image coder is constructed by recursive, least-squares piecewise functional approximation. The seemingly expensive encoding process can be made efficient by an incremental least-squares computation technique. The decoding is simple and can be done in real time if assisted by existing hardware technology.
Near-lossless image compression schemes based on weighted finite automata encoding and adaptive context modelling

We study high-fidelity image compression with a given tight bound on the maximum error magnitude. We propose a weighted finite automata (WFA) recursive encoding scheme on the adaptive context modelling based quantizing prediction residue images. By incorporating the proposed recursive WFA encoding techniques into the context modelling based nearly-lossless CALIC (context based adaptive lossless image codec), we were able to increase its PSNR by 1.5 dB or more and get compression rates 15 per cent or better than the original CALIC. By combining wavelet methods and WFA encoding, we were able to obtain competitive PSNR results against the best wavelet coders in both L/sub 2/ and L/spl infin/ metrics, while obtaining much smaller maximum error magnitude than the latter.
On Computation of Performance Bounds of Optimal Index Assignment

Channel-optimized index assignment of source codewords is arguably the simplest way of improving transmission error resilience, while keeping the source and/or channel codes intact. But optimal design of index assignment is an instance of quadratic assignment problem (QAP), one of the hardest optimization problems in the NP-complete class. In this work we make a progress in the research of index assignment optimization. We apply some recent results of QAP research to compute the strongest lower bounds so far for channel distortion of BSC among all index assignments. The strength of the resulting lower bounds is validated by comparing them against the upper bounds produced by heuristic index assignment algorithms.
On Properties of Locally Optimal Multiple Description Scalar Quantizers With Convex Cells

It is known that the generalized Lloyd method is applicable to locally optimal multiple description scalar quantizer (MDSQ) design. However, it remains unsettled when the resulting MDSQ is also globally optimal. We partially answer the above question by proving that for a fixed index assignment there is a unique locally optimal fixed-rate MDSQ of convex cells under Trushkin's sufficient conditions for the uniqueness of locally optimal fixed-rate single description scalar quantizer. This result holds for fixed-rate multiresolution scalar quantizer (MRSQ) of convex cells as well. Thus, the well-known log-concave probability density function (pdf) condition can be extended to the multiple description and multiresolution cases.
Context selection and quantization for lossless image coding

After the context quantization, an entropy coder using L2/sup K/ (L is the quantized levels and K is the number of bits) conditional probabilities remains impractical. Instead, only the expectations are approximated by the sample means with respect to different quantized contexts. Computing the sample means involves only cumulating the error terms in the quantized context C(d,t) and keeping a count on the occurrences of C(d,t). Thus, the time and space complexities of the described context based modeling of the prediction errors are O(L2/sup K/). Based on the quantized context C(d,t), the encoder makes a DPCM prediction I, adds to I the most likely prediction error and then arrives at an adaptive, context-based, nonlinear prediction. The error e is then entropy coded. The coding of e is done with L conditional probabilities. The results of the proposed context-based, lossless image compression technique are included.
Data-Driven Soft Decoding of Compressed Images in Dual Transform-Pixel Domain.

In the large body of research literature on image restoration, very few papers were concerned with compression-induced degradations, although in practice, the most common cause of image degradation is compression. This paper presents a novel approach to restoring JPEG-compressed images. The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images. The restoration is a sparse coding process carried out jointly in the DCT and pixel domains. The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time, using online machine-learned local spatial features to regulate the solution of the underlying inverse problem. Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.
Combining information display and visible light wireless communication

This paper opens up an unforeseen and intriguing application area of our recent pioneer work of temporal psychovisual modulation: wireless optical communication. It is demonstrated how a high-speed optoelectronic display functions as a 2D array of optical transmitters and at the same exhibits conventional images as usual. At the receiving end, digital cameras can download data via the optical MIMO link while user(s) can work and read the display as they are accustomed. The said unification of information display and wireless optical communication is made possible by psychovisually based image processing.
On Properties of Discretized Convex Curves

The connection between a continuous convex curve and its discrete image is investigated using an appropriate definition of discrete convexity. It is shown that the discrete image of continuous convex curve may not be convex; however, its deviation from convexity (in the discrete definition) is bounded by a small constant. The actual pixel patterns that are obtained by discretizing convex curves are studied. Certain constraints on the context of discrete images of continuous convex curves were discovered.
Globally Optimal Uneven Erasure-Protected Multi-Group Packetization of Scalable Codes

We study the problem of rate-distortion optimal packetization with uneven erasure protection (UEP) of scalable source sequence, into multiple groups of packets. The grouping of packets is needed when the length of the channel code, hence the number of packets, has to be modest for low decoding complexity. The problem was previously addressed in the literature but only locally optimal solution was proposed. We develop an algorithm for globally optimal solution and show that it has the same complexity as optimal UEP packetization into a single group of packets, i.e., quadratic in transmission budget
Rainbow network problems and multiple description coding

In packet switched networks receivers can get packets of a multiple description code (MDC) from different sources for enhanced QoS and robust transmission. The quality achieved by a decoder increases in the number of distinct rather than the total number of packets received. This property makes the problems of optimizing network flows and transmission strategies for MDC, called rainbow network problems, very different from those of conventional network flow and management. Two interesting problems: rainbow network flow and rainbow multicast, are formulated and treated. The rainbow network flow problem of maximizing the number of distinct packets received, constrained by edge capacities, is shown to be NP-hard in multisource-multisink setting. But it can be reduced to conventional maximum network flow problem in the case of single sink, hence becomes solvable in polynomial time. Rainbow multicast problem is about coordinating multiple servers for minimum expected distortion at one or a set of clients. Although being seemingly intractable in general, some variants of the problem have analytical solutions
Improved techniques for lossless image compression with reversible integer wavelet transforms

The past few years have seen an increasing interest in using reversible integer wavelets in image compression. Reversible integer wavelet image coders facilitate decompression from low bit rates all the way up to lossless reconstruction. However, in the past, specific implementations of such techniques, like S+P, could not match the lossless compression performance of state-of-the-art predictive coding techniques like CALIC. We demonstrate for the first time that reversible integer wavelets together with proper context modeling can match the lossless compression performance of CALIC. This can be done without increase in the essential complexity over S+P. Our findings present a strong argument for using subband coding as a unified, elegant approach for both lossy and lossless image compression. Specifically, in this paper we outline how to obtain significantly higher coding efficiency over S+P by utilizing better filters and better context modeling and entropy coding of wavelet coefficients.
Multiple-description geometry compression for networked interactive 3D graphics

An existing technique for robust streaming of 3D graphics contents over lossy networks is multi-resolution coding of 3D geometry. An advantage of this approach is that it uses refinement layers and therefore multiple clients with different bandwidths can be served by a single unified code stream. However, there is a dependency between refinement layers, called prefix condition. Decoding of a given layer requires the knowledge of all the previous layers. A problem in the base layer reception interrupts the streaming all together and voids the remaining layers even though they are received perfectly. To overcome this drawback, this paper proposes an alternative approach to multi-resolution geometry coding, called multiple-description coding of 3D geometry. Instead of organizing code stream into embedded layers, MDC generates several separate descriptions of a geometric object, called co-descriptions. Each co-description of MDC can be independently decoded without any knowledge of other co-descriptions. Each extra successfully received co-description improves the fidelity of reconstructed geometry regardless of what has been received so far or in what order.
VQ index coding for high-fidelity medical image compression

In order to obtain the high-fidelity medical compressed images, a new compression scheme is proposed. Based on the stringent requirements on lossy medical image compression, we refine the context modeling for a given class of medical images and utilize the conditional entropy coding of the VQ index (CECOVI) scheme to code the MR head images. The experimental results show that the image-type-dependent CECOVI can achieve better rate-distortion performance than the state-of-art wavelet image coder SPIHT. This also implies that incorporating the conditional entropy coding strategy into the VQ process is an appropriate way for high-fidelity medical image compression.
On wavelet compression of self-similar processes

Self-similar stochastic processes are stochastic counterparts of deterministic fractals. Fractional Brownian motion (fBm) is a self-similar nonstationary Gaussian process originally proposed to model power-law behavior of power spectrum of long-range dependant (LRD) natural processes. Multiscale nature of wavelets make them natural candidates for analysis and synthesis of fractional Brownian motions. Despite wavelet compression being the method of choice for image compression, the performance of wavelet compression schemes are investigated for compressing fractional Brownian motions. Theoretical rate-distortion function of fBm is explicitly derived.
A Linear Programming Approach for Optimal Contrast-Tone Mapping

This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping. In a fundamental departure from the current practice of histogram equalization for contrast enhancement, the proposed approach maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints that suppress artifacts. The underlying contrast-tone optimization problem can be solved efficiently by linear programming. This new constrained optimization approach for image enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new approach over histogram equalization and its variants.
Single image haze removal using Gaussian mixture model and sparse optimization

Single image haze removal is an underdetermined inverse problem whose solution hinges on valid image priors or models. In this work, robust priors drawn from outdoor scene statistics are explored. Specifically, a Gaussian mixture model of chrominance distribution is proposed toward transmittance estimation and its physical validity is justified. In addition, a new sparsity-based optimization approach for transmittance image super-resolution/restoration is proposed, which makes a solid assumption that most outdoor object surfaces are piece-wise linear and thus the corresponding depth image is sparse in Laplacian space. Experimental results are given in proof of the remarkably improved visual quality of our new haze removal technique over its predecessors.
Searchable compressed representation of very sparse bitmaps

Very sparse bitmaps are used in a wide variety of applications, ranging from adjacency matrices in representation of large sparse graphs, representation of sparse space occupancy to book-keeping in databases. A method based on pruning of the binary space partition (BSP) tree in the minimal description length (MDL) principle for coding very sparse bitmaps was proposed. This new method for coding of sparse bitmaps meets seemingly competing objectives of good compression, the ability of conducting queries directly in the compression domain, and simple and fast decoding.
On Interpolation and Resampling of Discrete Data

This letter introduces a new representation of discrete signals based on the mathematical notions of functionals and continuous dual spaces. A new and more general sampling theorem is also suggested. Next, the problems of interpolating and resampling discrete signals are addressed; and a general solution using functional interpolation-which is applicable to many different settings-is proposed. Families of resampling filters dubbed de Boor-Ron filters that use de Boor-Ron interpolation are introduced, and their numerical realization is discussed. Some applications of this research are suggested
On optimality of JPEG2000 code stream

Arguably the most important and defining feature of JPEG 2000 image compression standard is its R-D optimized code stream of multiple progressive layers. This code stream is an interleaving of many scalable code streams of different sample blocks. In this paper we investigate the algorithms of optimizing the interleaving to minimize the expected distortion weighted by the probability distribution of operational rates in a given range, rather than for one or few fixed rates. We prove that the JPEG 2000 code stream constructed by EBCOT (embedded block coding of optimal truncation) is indeed optimal for uniform rate distribution function even if the individual scalable code streams have non-convex operational R-D curves. We also develop algorithms to optimize the JPEG 2000 code stream for exponential and Laplacian rate distribution functions.
Learning-Based Restoration of Backlit Images.

Backlighting is a commonly encountered ill illumination condition that can cause serious degradation of image quality. In this paper, we propose a learning-based spatially adaptive technique of optimal tone mapping to restore backlit images. Object surfaces illuminated from behind in a scene are detected by a soft binary classifier that is constructed via supervised learning. Two optimal tone mapping functions, one for backlit regions and the other for the remainder of the image, are used and their outputs are fused to restore illegible surface details in backlit regions and at the same time improve contrast in overexposed regions, if any. Experimental results demonstrate the superior performance of the proposed new technique over existing image enhancement techniques on backlit photographs.