nils-herrmann/clean_scirepeval huggingface dataset:
On multirate optimality of JPEG2000 code stream

Arguably, the most important and defining feature of the JPEG2000 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 reexamine the R-D optimality of JPEG2000 scalable code streams under an expected multirate distortion measure (EMRD), which is defined to be the average distortion weighted by a probability distribution of operational rates in a given range, rather than for one or few fixed rates. We prove that the JPEG2000 code stream constructed by embedded block coding of optimal truncation is almost optimal in the EMRD sense for uniform rate distribution function, even if the individual scalable code streams have nonconvex operational R-D curves. We also develop algorithms to optimize the JPEG2000 code stream for exponential and Laplacian rate distribution functions while maintaining compatibility with the JPEG2000 standard. Both of our analytical and experimental results lend strong support to JPEG2000 as a near-optimal scalable image codec in a fairly general setting.
Multiple Descriptions with Side Informations Also Known At the Encoder

We propose a new scheme of multiple descriptions with side information (SI). The two side decoders of the system use two different SI streams. Both SI streams are available to the central decoder and to the encoder. We give an inner bound for this system for general source and SI. The tight bound is obtained for the quadratic Gaussian case. This result is compared with our previous result of the MDWZ (multiple descriptions in the Wyner-Ziv setting) problem in which none of the side information is known at the encoder. It is shown that when side information is absent at the encoder, there is a performance loss. The proposed scheme and its achievable region have practical significance. It offers theoretical insight into the multiple description video coding (MDVC) and suggests an optimal coding strategy.
Adaptive Directional Lifting-Based Wavelet Transform for Image Coding

We present a novel 2-D wavelet transform scheme of adaptive directional lifting (ADL) in image coding. Instead of alternately applying horizontal and vertical lifting, as in present practice, ADL performs lifting-based prediction in local windows in the direction of high pixel correlation. Hence, it adapts far better to the image orientation features in local windows. The ADL transform is achieved by existing 1-D wavelets and is seamlessly integrated into the global wavelet transform. The predicting and updating signals of ADL can be derived even at the fractional pixel precision level to achieve high directional resolution, while still maintaining perfect reconstruction. To enhance the ADL performance, a rate-distortion optimized directional segmentation scheme is also proposed to form and code a hierarchical image partition adapting to local features. Experimental results show that the proposed ADL-based image coding technique outperforms JPEG 2000 in both PSNR and visual quality, with the improvement up to 2.0 dB on images with rich orientation features
Sparsity-based depth image restoration using surface priors and RGB-D correlations

In this paper we propose a sparsity-based, directional variational approach for upsampling depth images, aided by an accompanying optical (in RGB) image of higher spatial resolution. Compared to previously published works on RGB-D superresolution, the main innovations of this work are: 1. performing depth image restoration in an overcomplete sparsity space derived from the directionalities of the RGB image; 2. refining the regularization term of the underlying inverse problem by a cross-validating spatial discontinuities in the optical and depth images. By integrating these new techniques the proposed depth image superresolution method delivers very competitive performance against existing ones.
Optimal Multicast with Erasure Correction Coding

We propose a network information flow strategy of optimal multicast with erasure correction coding (OMEC), in which erasure correction is a means of utilizing diversities rather than combating packet losses. An information source is first encoded by an erasure correction code and then optimally multicast by a server to sinks. The OMEC scheme has the following characteristics: (1) It is at least as good as optimal routing and, for some cases, can realize a network throughput arbitrarily larger than that achievable by pure routing. (2) For most scenarios, the approach yields a throughput comparable with the optimal throughput achievable with network coding. In fact, for most examples in the literature for which network coding makes high throughput gains compared to pure routing, OMEC performs close to the optimal. (3) Unlike general network coding, intermediate nodes are only required to route information. This makes OMEC compatible with currently deployed network structures. (4) Surprisingly, unlike pure optimal routing, OMEC can be efficiently computed in polynomial time. While OMEC is, in general, suboptimal compared to full fledged network coding, it enjoys most of the computational and throughput benefits associated with network coding over pure routing, without requiring any changes to existing hardware infrastructures or protocol stacks
Joint image/video compression and encryption via high-order conditional entropy coding of wavelet coefficients

As the Internet and multimedia systems grow in size and popularity, compression and encryption of image and video data are becoming increasingly important. However, independent compression and encryption is too slow for many multimedia applications. This paper presents a new joint compression and encryption method for images and videos that uses high-order conditional entropy coding of wavelet coefficients to facilitate encryption. As a result, state-of-the-art compression and significantly enhanced security are achieved, with no extra computational complexity.
Structure Preserving Image Interpolation via Adaptive 2D Autoregressive Modeling

The performance of image interpolation depends on an image model that can adapt to nonstationary statistics of natural images when estimating the missing pixels. However, the construction of such an adaptive model needs the knowledge of every pixels that are absent. We resolve this dilemma by a new piecewise 2D autoregressive technique that builds the model and estimates the missing pixels jointly. This task is formulated as a non-linear optimization problem. Although computationally demanding, the new non-linear approach produces superior results than current methods in both PSNR and subjective visual quality. Moreover, in quest for a practical solution, we break the non-linear optimization problem into two subproblems of linear least-squares estimation. This linear approach proves very effective in our experiments.
Edge-Guided Perceptual Image Coding via Adaptive Interpolation

We propose a new image compression technique based on adaptive edge-guided interpolation. The design criterion of the new image compression technique is a perceptual one. We aim to preserve the spatial correlation structures of image edges because human visual system is highly sensitive to distortions of spatial coherence of edges. Experimental results show that the proposed technique indeed achieves superior visual quality than JPEG 2000 at low to modest bit rates. For situations when the encoder has limited computing resources and/or power supply (e.g., wireless communications and sensor networks), the new technique has the desirable property of low encoding complexity.
Quantitative steganalysis of binary images

We propose a quantitative steganalysis method to detect hidden information embedded by flipping pixels along boundaries in binary images. We model steganographic embedding as an additive noise process and use compression rate as a distinguishing statistic that aids in discriminating between stego-images and cover-images. We specifically use the JBIG 2 binary image compression algorithm to derive a quantitative relation between compression rate and embedding rate. Based on this relationship, a practical steganalysis technique is proposed by examining the change of compression rate as embedding rate increases. Experiments conducted show that the proposed technique can reliably detect a steganographic embedding process that flips boundary pixels. Furthermore, it can estimate embedding rate with reasonable accuracy.
Joint Source-Channel Decoding of Multiple Description Quantized and Variable Length Coded Markov Sequences

This paper proposes a framework for joint source-channel decoding of Markov sequences that are encoded by an entropy coded multiple description quantizer (MDQ), and transmitted via a lossy network. This framework is particularly suited for lossy networks of inexpensive energy-deprived mobile source encoders. Our approach is one of maximum aposteriori probability (MAP) sequence estimation that exploits both the source memory and the correlation between different MDQ descriptions. The MAP problem is modeled and solved as one of the longest path in a weighted directed acyclic graph. For MDQ-compressed Markov sequences impaired by both bit errors and erasure errors, the proposed joint source-channel MAP decoder can achieve 5 dB higher SNR than the conventional hard-decision decoder. Furthermore, the new MDQ decoding technique unifies the treatments of different subsets of the K descriptions available at the decoder, circumventing the thorny issue of requiring up to 2K-1 MDQ side decoders
Video Restoration Against Yin-Yang Phasing

A common video degradation problem, which is largely untreated in literature, is what we call Yin-Yang Phasing (YYP). YYP is characterized by involuntary, dramatic flip-flop in the intensity and possibly chromaticity of an object as the video plays. Such temporal artifacts occur under ill illumination conditions and are triggered by object or/and camera motions, which mislead the settings of camera's auto-exposure and white point. In this paper, we investigate the problem and propose a video restoration technique to suppress YYP artifacts and retain temporal consistency of objects appearance via inter-frame, spatially-adaptive, optimal tone mapping. The video quality can be further improved by a novel image enhancer designed in Weber's perception principle and by exploiting the second-order statistics of the scene. Experimental results are encouraging, pointing to an effective, practical solution for a common but surprisingly understudied problem.
An algorithmic study on context modelling for lossless image compression

Statistical context modelling is a powerful and versatile technique for lossless image coding. A key issue in context modelling is how to increase the order of model without drastically increasing the model cost. We take an algorithmic approach to address the issue and propose a few heuristical optimization techniques that fine tune models of relatively few parameters in the sense of entropy minimization. Our experimental results indicate that these techniques are quite effective and achieve the lowest lossless bit rates so far over a variety of test images.
Optimal alphabet partitioning for semi-adaptive coding of sources of unknown sparse distributions

Practical applications that employ entropy coding for large alphabets often partition the alphabet set into two or more layers. Each symbol was encoded using suitable prefix coding for each layer. The problem of optimal alphabet partitioning was formulated for the design of a two layer semi-adaptive code and the given solution was based on dynamic programming. However, the complexity of the dynamic programming approach can be quite prohibitive for a long sequence and very large alphabet size. Hence, a simple greedy heuristic algorithm whose running time is linear in the number of symbols being encoded was given, irrespective of the underlying alphabet size. The given experimental results demonstrated the fact that superior prefix coding schemes for large alphabets can be designed using this approach as opposed to the typically ad-hoc partitioning approach applied in the literature.
Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation

The performance of the classic split-and-merge segmentation algorithm is severely hampered by its rigid split-and-merge processes, which are insensitive to the image semantics. The author proposes efficient algorithms and data structures to optimize the split-and-merge processes by piecewise least-square approximation of image intensity functions. This optimization aims at the unification of segment finding and edge detection. The optimized split-and-merge algorithm is shown to be adaptive to the image semantics and, hence, improves the segmentation validity of the previous algorithms. This algorithm also appears to work well on noisy sources. Since the optimization is done within the split-and-merge framework, the better segmentation performance is achieved at the same order of time complexity as the previous algorithms.
Analysis of trellis quantization for near-lossless image coding

We discuss several variations to the original algorithm proposed by Ke and Marcellin (see Proc.  1995). We have extended the trellis quantization (TQ) scheme by performing two-row joint optmizations instead of optimizing row by row. Unfortunately, while increasing the computation time quite a bit, this has lead only to marginal coding gains. A progressive probability update scheme has lead to much better convergence and to a 0.3 bpp gain over the original fixed scheme. When using lossy plus near-lossless coding the lossy version can be used for better context modelling without increasing the computational complexity of the near-lossless residual coding. Improvements of 0.1-0.2 bpp were observed. Since it is computationally infeasible to include more pixels to be determined by the TQ process, one has the choice of either using better prediction/context-modelling or doing TQ. Our tests indicate that the preference should be given to sophisticated prediction/modelling.
Lossless compression of continuous-tone images via context selection, quantization, and modeling

Context modeling is an extensively studied paradigm for lossless compression of continuous-tone images. However, without careful algorithm design, high-order Markovian modeling of continuous-tone images is too expensive in both computational time and space to be practical. Furthermore, the exponential growth of the number of modeling states in the order of a Markov model can quickly lead to the problem of context dilution; that is, an image may not have enough samples for good estimates of conditional probabilities associated with the modeling states. New techniques for context modeling of DPCM errors are introduced that can exploit context-dependent DPCM error structures to the benefit of compression. New algorithmic techniques of forming and quantizing modeling contexts are also developed to alleviate the problem of context dilution and reduce both time and space complexities. By innovative formation, quantization, and use of modeling contexts, the proposed lossless image coder has a highly competitive compression performance and yet remains practical.
Length-constrained MAP decoding revisited

We consider the problem of length-constrained maximum a posteriori (MAP) decoding of a Markov sequence that is variable length encoded and transmitted over a binary symmetric channel. We convert this problem into one of 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 previous heuristic algorithms.
Clustering by principal curve with tree structure

Data clustering is intensively used in signal processing in tasks such as multimedia compression, segmentation and pattern matching. In this work we extend the use of principal curves in clustering to complex multidimensional datasets. The use of principal curve in clustering is limited for high complexity data. Automatic parameterization of the principal curve to assure good results for different datasets is a difficult task. We propose to use the tree structure to capture the general settlement of the data. Using this topology, regions of the dataset can be extracted, individually clustered using the principal curve and then optimally recombined. The experiments show the improvement of the new method over the principal curve based clustering and the good performance compared to other clustering methods.
Optimal unequal channel protection of multiple-description product codes for multimedia communications over fast fading channels

In recent literature a powerful multiple description product coding scheme for protection of progressively encoded source streams has been devised that disperses information evenly between all description packets. Also, techniques were proposed to protect these packets equally by an optimal channel coder (found by exhaustive search). The contribution of this paper is to show that equal protection of all descriptions is suboptimal when the channel varies with time despite the fact that all descriptions have equal importance. We propose a theoretical framework for computing the globally optimal channel protection assignment for a given set of available channel coders under some idealized assumptions. For more practical scenarios we propose an optimized uneven packet protection scheme that outperforms equal protection schemes in terms of the expected distortion of received sources. Simulations of an image transmission system that resembles a 3G high bitrate link is provided where our unequal protection scheme improves the average PSNR of the received images by more than 1.3dB.
On optimal multi-resolution scalar quantization

Any scalar quantizer of 2/sup h/ bins, where h is a positive integer, can be structured by a balanced binary quantizer tree T of h levels. Any pruned subtree /spl tau/ of T corresponds to an operational rate R(/spl tau/) and distortion D(/spl tau/) pair. Denote by S/sub n/ the set of all pruned subtrees of n leaf nodes, 1/spl les/n/spl les/2/sup h/. We consider the problem of designing a 2/sup h/-bin scalar quantizer that minimizes the weighted average distortion D~=/spl Sigma//sub n=1//sup 2(h)/ D(/spl tau/)W(n), where W(n) is a weighting function in the size of pruned subtrees (or the resolution of the underlying quantizer). We present an O(hN/sup 3/) algorithm to solve the underlying optimization problem (N is the number of points of the histogram that represents the source probability mass function), and call the resulting quantizer optimal multi-resolution scalar quantizer in the sense that it minimizes a global distortion measure averaged over all quantization resolutions of T. Interestingly, a similar quantizer design problem studied by Brunk et al. (1996) is a special case of our formulation, and can thus be solved exactly and efficiently using our algorithm. Furthermore, we present an algorithm to generate a sequence of 2/sup h/ nested pruned subtrees of T, from the root of T to the entire tree T itself, which minimizes an expected distortion over a range of operational rates. The resulting nested pruned subtree sequence generates an optimized embedded (rate-distortion scalable) code stream with maximum granularity of 2/sup h/ quantization stages, as opposed to existing successively refinable quantizers, such as the popular bit-plane coding scheme, which offer only h stages.
Joint denoising and contrast enhancement of images using graph laplacian operator

Images and videos are often captured in poor light conditions, resulting in low-contrast images that are corrupted by acquisition noise. To recreate a high-quality image for visual observation, the captured image must be denoised and contrastenhanced. Conventional methods perform these two tasks in two separate stages: an image is first denoised, followed by an enhancement procedure. In this paper, we propose to jointly denoise and enhance an image in one unified optimization framework. The crux of the optimization rests on the definition of the enhancement operator, described by a graph Laplacian matrix H. The operator must enhance the high frequency details of the original image without amplifying additive noise. We propose a graph-based low-pass filtering approach to denoise edge weights in the graph, resulting in a more robust estimate of H. Experimental results show that our proposed joint approach can outperform the separate approach in demonstrable image quality.
Optimal binary vector quantization via enumeration of covering codes

Binary vector quantization (BVQ) refers to block coding of binary vectors under a fidelity measure. Covering codes were studied as a means of lattice BVQ. But a further source coding problem hidden in the equivalence of covering codes has seemingly eluded attention. Given a d-dimensional hypercube (code space), equivalent covering codes of the same covering radius but of different codewords have different expected BVQ distortions for a general probability mass function. Thus one can minimize, within the code equivalence, the expected distortion over all different covering codes. This leads a two-stage optimization scheme for BVQ design. First we use an optimal covering code to minimize the maximum per-vector distortion at a given rate. Then under the minmax constraint, we minimize the expected quantization distortion. This minmax constrained BVQ method (MCBVQ) controls both the maximum and average distortions, and hence improves subjective quality of compressed binary images, MCBVQ also avoids poor local minima that may trap the generalized Lloyd method. The [7,4] Hamming code and [8,4] extended Hamming code are found to be particularly suitable for MCBVQ on binary images. An efficient and simple algorithm is introduced to enumerate all distinct [7,4] Hamming/[8,4] extended Hamming codes and compute the corresponding expected distortions in optimal MCBVQ design. Furthermore, MCBVQ using linear covering codes has a compact codebook and a fast syndrome-encoding algorithm.
Lagrangian global optimization of two-description scalar quantizers

We develop an efficient Lagrangian-type algorithm for optimal two-description fixed-rate scalar quantizer design, for a very large class of distortion measures. Our key result is the discovery that the Lagrangian multiplier for the globally optimal solution exists. Although Lagrangian optimization is a method of choice for quantizer design, none of the previous algorithms using this method was shown to guarantee the global optimality for any instance of the problem