nils-herrmann/clean_scirepeval huggingface dataset:
Globally optimal uneven error-protected packetization of scalable code streams

In this paper, we present a family of new algorithms for rate-fidelity optimal packetization of scalable source bit streams 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 obtained O(N/sup 3/L/sup 2/) complexity, where N is the number of packets and L is the packet payload size. If the rate-fidelity function of the input is convex, the time complexity can be reduced to O(NL/sup 2/) for a class of erasure channels, including channels for which the probability function of losing n packets is monotonically decreasing in n and independent erasure channels with packet erasure rate no larger than N/2(N + 1). Furthermore, our O(NL/sup 2/) algorithm for the convex case can be modified to rind an approximation solution for the general case. All of our algorithms do away with the expediency of fractional bit allocation, a limitation of some existing algorithms.
Optimal Backlight Modulation With Crosstalk Control in Stereoscopic Display

Crosstalk between the left-eye and right-eye images is one of the main artifacts affecting the visual quality of stereoscopic liquid crystal display (LCD) systems. In this paper, a novel technique, called Optimal Backlight Modulation (OBM), is proposed to reduce crosstalk by taking the advantage of newly emerged light-emitting diode (LED) backlight panel. Unlike most existing techniques which solely aim at eliminating the effect of crosstalk, ignoring image brightness and contrast, OBM strikes a better balance by maximizing the output luminance while confining the crosstalk within a given level. A simple closed-form approximation of the optimization problem can be easily employed and solved in real time on LCD control hardware. Simulation results show that the proposed OBM algorithm provides the same or higher luminance while reducing the crosstalk by 60% compared with the other tested methods.
Resource-Scalable Joint Source-Channel MAP and MMSE Estimation of Multiple Descriptions

A joint source-channel multiple description (JSC-MD) framework for signal estimation and communication in resource-constrained lossy networks is presented. To keep the encoder complexity at a minimum, a signal is coded by a multiple description quantizer (MDQ) with neither entropy nor channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to combat transmission errors. A key design objective is resource scalability: powerful nodes in the network can perform JSC-MD estimation under the criteria of maximum a posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MAP estimator is an algorithm of the longest path in a weighted directed acyclic graph, while the JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ and the inter-description correlations. They outperform the existing hard-decision MDQ decoders by large margins (up to 8 dB). For Gaussian Markov sources, the complexity of JSC-MD distributed MAP sequence estimation can be made as low as that of typical single description Viterbi-type algorithms.
Temporal color video demosaicking via motion estimation and data fusion

Color demosaicking of charge-coupled device (CCD) data has been thoroughly studied for single-sensor still digital cameras. However, there has seemingly been little research on color demosaicking techniques for single-sensor video digital cameras. The temporal dimension of a color mosaic image sequence can reveal new information on the missing color components due to the mosaic subsampling, which is otherwise unavailable in the spatial domain of individual frames. This paper proposes a temporal approach to color demosaicking. A pixel of the current frame is matched to another in a reference frame via motion analysis, such that the CCD sensor samples different color components of the same object position in the two frames. The resulting inter-frame estimates of missing color components are fused with suitable intra-frame estimates to achieve a more robust color restoration. Our experimental results demonstrate clear advantages of the presented temporal color demosaicking approach over its intra-frame counterparts in reducing the color artifacts.
L2 restoration of L∞-decoded images with context modeling

The L∞-constrained image coding is a technique to achieve substantially lower bit rate than strictly (mathematically) lossless image coding while still imposing a tight error bound at each pixel (colloquially referred to as near-lossless image coding). However, this technique becomes inferior in the L2 distortion metric if the bit rate decreases further. We propose a new soft decoding approach to reduce the L2 distortion of L∞-coded images, benefiting from the advantages of both minmax and mean square approximations. This is made possible by context modeling of quantization distortions and by exploiting the L∞ bound inherent to near-lossless coding in a framework of image restoration. In addition, the proposed soft decoding approach offers an asymmetric high-fidelity image compression solution: the encoder is of low complexity with heavy computations of gaining coding efficiency performed by the decoder. Experimental results demonstrate that the new soft decoding approach can improve the PSNR of L∞-decoded images by more than 1 dB, and it can even outperform JPEG 2000 (a state-of-the-art encoder-optimized image codec) for bit rates higher than 1.17 bpp, while achieving much tighter L∞ error bound.
Piecewise 2D autoregression for predictive image coding

We propose an on-line piecewise two-dimensional autoregression algorithm (P2AR) for modeling and coding of images. The algorithm assumes no stationarity of the source. The resulting P2AR predictor can be loosely considered as a universal parametric image model in the sense that no prior knowledge about the image are assumed. The predictor parameters are causal and dynamically adapted to the source, hence suits one-pass image coding with no side information. The good fit of the model to a variety of images is demonstrated by its superior performance when being used in lossless image coding. We reduced the bit rate of the benchmark lossless image codec CALIC by three percent averaging over the JPEG set of test images, simply by replacing the context-sensitive, non-linear GAP predictor of CALIC with the P2AR predictor. The performance margin gets much larger when the source is highly nonstationary. In order to make on-line 2D autoregression computationally feasible, we developed a novel and efficient algorithm for computing and updating covariances of regression variables on a pixel-by-pixel basis. This algorithm can also be applied in image segmentation, texture classification, and other image analysis tasks that require heavy computations of second order statistics.
Context-based, adaptive, lossless image coding

We propose a context-based, adaptive, lossless image codec (CALIC). The codec obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. The CALIC puts heavy emphasis on image data modeling. A unique feature of the CALIC is the use of a large number of modeling contexts (states) to condition a nonlinear predictor and adapt the predictor to varying source statistics. The nonlinear predictor can correct itself via an error feedback mechanism by learning from its mistakes under a given context in the past. In this learning process, the CALIC estimates only the expectation of prediction errors conditioned on a large number of different contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach, nor from excessive memory use. The low time and space complexities are also attributed to efficient techniques for forming and quantizing modeling contexts.
Image interpolation with hidden Markov model

We propose an adaptive image interpolation technique based on hidden Markov modeling (HMM) and maximum a posterior (MAP) estimation. The HMM incorporates the statistics of high resolution (HR) images into the interpolation process and the MAP estimation exploits high-order statistical dependency between pixels. Experimental results show that the HMM-based image interpolation technique can reproduce cleaner and sharper image details than its predecessors, while suppressing common interpolation artifacts such as ringing, jaggies, and blurring.
On the Sum Rate of Gaussian Multiterminal Source Coding: New Proofs and Results

We show that the lower bound on the sum rate of the direct and indirect Gaussian multiterminal source coding problems can be derived in a unified manner by exploiting the semidefinite partial order of the distortion covariance matrices associated with the minimum mean squared error (MMSE) estimation and the so-called reduced optimal linear estimation, through which an intimate connection between the lower bound and the Berger-Tung upper bound is revealed. We give a new proof of the minimum sum rate of the indirect Gaussian multiterminal source coding problem (i.e., the Gaussian CEO problem). For the direct Gaussian multiterminal source coding problem, we derive a general lower bound on the sum rate and establish a set of sufficient conditions under which the lower bound coincides with the Berger-Tung upper bound. We show that the sufficient conditions are satisfied for a class of sources and distortion constraints; in particular, they hold for arbitrary positive definite source covariance matrices in the high-resolution regime. In contrast with the existing proofs, the new method does not rely on Shannon's entropy power inequality.
Rainbow Network Flow with Network Coding

Rainbow network flow is a recent enquiry into rate-distortion optimized network flow of a multiple description (MD) coded source from multiple encoders to multiple sinks. This paper is a first attempt to combine network coding with rainbow network flow, and formulates a new problem called rainbow network coding (RNC). It is shown that performing linear network coding at intermediate nodes of a flow path can achieve higher network throughput than the original rainbow network flow solution. RNC is related to the linear broadcast problem defined in network coding theory, but it has a different objective function that is more meaningful for lossy signal compression applications than linear broadcast. We prove the RNC problem to be NP-hard, and propose some heuristic solutions. Examples are presented to study the differences between rainbow network coding, rainbow network flow, and linear broadcast.
On global optimality of gradient descent algorithms for fixed-rate scalar multiple description quantizer design

We prove that Trushkin's (1982) sufficient conditions for the global optimality of a locally optimal fixed-rate scalar quantizer also ensure the global optimality of a locally optimal fixed-rate multiple description scalar quantizer of convex codecells, with respect to a fixed index assignment. This result also holds for the fixed-rate multiresolution scalar quantizer of convex codecells. As a consequence the well-known log-concave pdf condition can be extended to the multiple description and multiresolution case.
On Two-Stage Sequential Coding of Correlated Sources

We study the problem of two-stage sequential coding (TSSC), which is an extension of sequential coding of correlated sources. Let X and Y be dependent random variables. The network contains two encoders and two decoders: 1) a Y encoder with input Y; 2) an X encoder with inputs X and Y; 3) a Y decoder that reconstructs Y; and 4) an X decoder that reconstructs X. The first stage is traditional sequential coding, where the Y encoder describes Y to both the X decoder and Y decoder, and the X encoder describes X and Y to the X decoder. At the second stage, the Y encoder refines the description of Y, and the X encoder refines the description of X. The TSSC model is a theoretical abstraction of scalable video coding; here, Y and X represent successive frames of a video sequence, and the two stages together give an embedded description that allows the video to be decoded at two distinct rates. We give an inner bound on the rate distortion region for this TSSC model. The tight bound on the rate distortion region is derived when Y must be reconstructed losslessly (in the usual Shannon sense) in the second stage. We also study the minimum total rate of the TSSC model and show that the minimum total rate of one-stage sequential coding cannot be achieved at both stages for jointly Gaussian sources. This theoretical result can shed light on the rate-distortion performance behavior of scalable video coding widely noted by practitioners.
Joint source-channel decoding of multiple description quantized Markov sequences

This paper proposes a framework for joint source-channel decoding of Markov sequences that are coded by a fixed-rate multiple description quantizer (MDQ), and transmitted via a lossy network. This framework is suited for lossy networks of primitive energy-deprived source encoders. Our technical approach is one of maximum a posteriori probability (MAP) sequence estimation that exploits both the source memory and the correlation between different MDQ descriptions. We solve the MAP estimation problem by computing the longest path in a weighted directed acyclic graph, at a complexity of O(L/sup 2/NK), where N is the number of source symbols in the input sequence, K is the number of MDQ descriptions, and L is the number of codewords of the central quantizer. If the source sequence is Gaussian Markovian, the decoder complexity can be reduced to O(LNK). For MDQ-compressed Markov sequences impaired by both bit errors and erasure errors, the performance of joint source-channel MAP decoder can be 6 dB higher 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 2/sup K/ - 1 MDQ side decoders.
On embedded scalable code stream of minimum expected distortion for variable rate channels

We consider the problem of constructing an embedded scalable code stream from a given tree structured quantizer (TSQ) to minimize the expected distortion weighted over a range of operational rates. If the rate weighting function is exponential, the problem is solvable by a simple algorithm reminiscent to merge sort. The complexity of the algorithm is O(NlogN) where N is the number of internal nodes of the corresponding quantizer tree.
Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain

Arguably the most common cause of image degradation is compression. This papers 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 jointy 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 on-line machine-learnt 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.
Can Lower Resolution Be Better?

Recently, many researchers started to question a long-standing paradox in the engineering practice of digital photography: oversampling followed by compression, and pursue more intelligent sparse sampling techniques. In this research we take a practical approach of uniform down sampling in image space, and the sampling is made adaptive by a spatially varying directional low-pass prefiltering. Since the down-sampled prefiltered image is a low-resolution image of conventional square sample grid, it can be compressed and transmitted without any change to current image coding standards and systems. The decoder first decompresses the low-resolution image and then upsamples it to the original resolution by least-square estimation using a 2D piecewise autoregressive model and the knowledge of directional low-pass filter. The proposed joint adaptive down-sampling and up-sampling technique outperforms JPEG 2000 (the state-of-the-art in lossy image coding) in PSNR measure at low to modest bit rates and achieves superior visual quality at all bit rates. This work shows that oversampling not only increases cost and energy consumption, but it could, even when coupled with a sophisticated rate-distortion optimized compression scheme, cause inferior image quality at certain bit rates.
Channel model proposal for indoor relay-assisted power line communications

Due to its tree-like topology, the point-to-point (P2P) indoor power line communication (PLC) channel exhibits a similar broadcasting property as the wireless propagation channel shows. However, after relay nodes are introduced, the relay-assisted PLC (RaPLC) channel shows some notable differences compared with its wireless counterpart. A lot of results about the relay-assisted wireless channel can be found in the literature, but the work of modelling RaPLC channel has rarely been discussed in detail. As the first attempt to fill this gap, we apply the ABCD method to compute the channel transfer function (CTF) of the RaPLC channel. It has been clarified that, in general, the CTF of RaPLC channel cannot be obtained as a cascade of two independent P2P PLC sub-channels located on two sides of the relay node. Also, it is shown that the challenge of generating CTF of the RaPLC channel can be transformed into a group of equivalent P2P PLC channel modelling tasks. Under the time-division duplexing constraint, the relay node changes its working mode from the receiving phase to the transmitting phase, which shows that the RaPLC channel is abruptly time-varying in nature. Following the hybrid bottom-up approach, a statistical channel model with a simple six-segment indoor power grid topology has been developed for simulation. Practical measurements and numerical examples verify our results.
Lossless interframe image compression via context modeling

In this paper, we present an interband version of CALIC (context-based adaptive lossless image codec), a lossless image coding technique. It is demonstrated that CALIC's techniques of context-based modeling of images lend themselves easily to modeling of image sequences. The generalized interframe CALIC can exploit both interframe and intraframe statistical redundancies, and obtain significant compression gains over intraframe CALIC. The advantage of interframe CALIC is demonstrated by experimental results on different types of multispectral images.
Non-embedded wavelet image coding scheme

A novel wavelet image coding scheme is presented. Significant discrete wavelet transformed coefficients are first selected with a threshold. Then, these significant coefficients are quantized with a uniform quantizer and decomposed into two parts: the most significant bit and the residual bits for entropy encoding. Simple but effective context modeling schemes are proposed for better squeezing of the redundancy lying in the significant map symbol stream determined by the threshold operation and the MSB symbol stream decomposed from the quantized significant coefficients. With these innovations, the proposed coding scheme outperforms all zerotree-structured embedded wavelet coding schemes and is competitive with other advanced coding algorithms reported in the literature.
Low complexity high-order context modeling of embedded wavelet bit streams

In the past three or so years, particularly during the JPEG 2000 standardization process that was launched last year, statistical context modeling of embedded wavelet bit streams has received a lot of attention from the image compression community. High-order context modeling has been proven to be indispensable for high rate-distortion performance of wavelet image coders. However, if care is not taken in algorithm design and implementation, the formation of high-order modeling contexts can be both CPU and memory greedy, creating a computation bottleneck for wavelet coding systems. In this paper we focus on the operational aspect of high-order statistical context modeling, and introduce some fast algorithm techniques that can drastically reduce both time and space complexities of high-order context modeling in the wavelet domain.
Multiple Descriptions in the Wyner-Ziv Setting

We propose a new scheme of multiple descriptions in the Wyner-Ziv setting (MD-WZ). The two side decoders of MD-WZ use two different side information (SI) streams. Both SI streams are available to the central decoder, but none to the encoder. We derive an achievable region (inner bound) for this MD-WZ system for general source and SI. If the source and SI are correlated Gaussian and for quadratic distortion metric, the tight bound is obtained. Our result is an extension of Ozarow's result on multiple descriptions of Gaussian source without SI. The MD-WZ coding scheme is shown to have a property of practical significance. For symmetric case where the joint distributions of the source and the two SI are the same and the two channels are balanced, interchanging the two channels causes no performance loss for Gaussian source. Considering that the existing multi-description video coding methods suffer from the notorious drifting problem induced by channels interchange, this work lends a theoretical support to distributed multi-description video coding in the Wyner-
Steganalysis of Degraded Document Images

In this paper, a steganalysis technique using compression bit rate as a distinguishing statistic is presented to detect secret messages embedded in document images that are degraded in quality by printing, photocopying, and/or scanning processes. We consider embedding techniques that flip pixels in binary document images that contain characters and symbols. Noise introduced by printing, photocopying, and/or scanning can be modeled by a local optical distortion process. Steganographic embedding is modeled as an additive noise process and we use compression bit rate as a distinguishing statistic to discriminate between stego images and unmarked images. Experimental results showed that the proposed technique can detect stego images with reasonably good accuracy, given the inherent difficulty of the problem
A practical approach to joint network-source coding

We are interested in how to best communicate a real valued source to a number of destinations (sinks) over a network with capacity constraints in a collective fidelity metric over all the sinks, a problem which we call joint network-source coding. It is demonstrated that multiple description codes in conjunction with proper diversity routing provide a powerful solution to joint network-source coding. A systematic optimization approach is proposed. It consists of optimizing the network routing given a multiple description code and designing optimal multiple description code for the corresponding optimized routes.