nils-herrmann/clean_scirepeval huggingface dataset:
Broadcasting with fidelity criteria

Consider the problem of broadcasting an i.i.d. source sequence X = {X/sub i/} /sub i=1//sup N/ (possibly N /spl rarr/ /spl infin/) to n listeners over a discrete broadcast channel, consisting of n channels with capacities C/sub 1/ = C/sub max/ /spl ges/ C/sub 2/ /spl ges/.../spl ges/ C/sub n/ = C/sub min/. Let the tuple D = (D/sub 1/, D/sub 2/,...,D/sub n/) represent the average distortion in reconstructing sources at the n listeners. The problem of characterizing all achievable tuples D is still open for a general case. For a fairly general class of discrete channels, we prove the achievability of the tuple n(/spl rho//sub 1/,/spl rho//sub 2/,...,/spl rho//sub n/) = (D/sub X/(/spl rho//sub 1/C/sub 1/ /spl zeta/), D/sub X/(/spl rho//sub 2/C/sub 2/ - /spl zeta/),...,D/sub X/(/spl rho//sub n/C/sub n/ - /spl zeta/)), provided that /spl lambda//sub i/ = (/spl rho//sub i/C/sub i/ - /spl rho//sub i/+/sub 1/C/sub i+1/)/C/sub i/ > 0, for 1 /spl les/ i /spl les/ n $1, /spl lambda//sub n/ = /spl rho//sub n/ and /spl Sigma//sub i=1//sup n-1/ /spl lambda//sub i/ /spl les/ 1, where D/sub X/ (R) is the distortion rate function of X. The penalty term /spl zeta/ = 1/2 for a general source with real alphabets and is /spl zeta/ = 0 if X is progressively refinable. The factor 0 < /spl rho//sub i/ /spl les/ 1 is called the utilization of the i/sup th/ channel. As an example, for n = 2, we show that /sup 2/(1/(2 - C/sub 2//C/sub 1/), 1/(2 $C/sub 1//C/sub 2/)) is achievable for any C/sub 1/, C/sub 2/. Furthermore, the common utilization of /spl rho/ = (1 + ln(C/sub max//C/sub min/))/sup -1/ is shown to be achievable for all channels. Conversely, we find examples of channels, namely erasure switch-to-talk channels, for which the proposed achievable utilizations are tight. In particular, while /sup 3/ (2/3, 2/3, 2/3) is achievable for any compatible broadcast channel with capacities C/sub 1/ = 2C/sub 2/ = 2C/sub 3/, for any /spl delta/ > 0, we find examples of channels for which /sup 3/(2/3+/spl delta/,2/3+/spl delta/,2/3+ /spl delta/) is not achievable.
Optimal power allocation for non-regenerative multicarrier relay-assisted PLC systems with QoS constraints

In this paper we present an iterative algorithm to jointly optimize the source and relay power allocations for a general relay transmission system, i.e. the broadcast-and-multiaccess (BMA) multicarrier relay system, where the source transmits in two time-slots. Under the indoor power line communication environment, we examine the issue of minimization of the total network transmission power subjecting to QoS constraints expressed as the capacity lower-bound of the data-link from the source to the destination nodes. In addition to demonstrate the fast convergence of the proposed algorithm, the simulation results also show that with respect to the two-hop relay system and the broadcast-and-forward (BF) relay system, the proposed general relay system can satisfy the same QoS requirement while consume less total transmission power.
Efficient Algorithm for Globally Optimal Uneven Erasure-Protected Packetization of Scalable Code Streams

A new algorithm is presented for rate-fidelity optimal packetization of scalable source bit streams with uneven erasure protection. It provides the globally optimal solution for input sources of convex rate-fidelity function and for a wide class of erasure channels, including channels for which the probability of losing n packets is monotonically decreasing in n, and independent erasure channels with packet erasure rate smaller than 0.5. The time and space complexities of the new algorithm are both O(NL), where N is the number of packets and L is the packet payload size, comparing to the O(NL2 ) time and space complexities of the existing globally optimal solution. When applied to SPIHT compressed images, the results of the proposed algorithm are virtually the same as the globally optima
A comparison of prediction schemes proposed for a new lossless image compression standard

It has long been realized that the current JPEG standard does not provide state-of-the-art performance in its lossless mode. In view of this, the International Standards Organization (ISO) recently solicited proposals for a new lossless/nearly lossless compression standard for continuous-tone still images. A total of nine proposals were submitted in the summer of 1995. Seven of these used a prediction step for 'decorrelating' the image prior to modelling and encoding. In this paper we investigate the efficacy of the different prediction schemes that were proposed. We also discuss their computational complexity and the price/performance trade-offs that emerge from our study.
Progressive coding of medical volumetric data using three-dimensional integer wavelet packet transform

Examines progressive lossy-to-lossless compression of medical volumetric data using 3D integer wavelet packet transforms and set partitioning in hierarchical trees (SPIHT). To achieve good lossy coding performance, we describe a 3D integer wavelet packet transform that allows implicit bit shifting of wavelet coefficients to approximate a 3D unitary transformation. We also address context modeling for efficient entropy coding within the SPIHT framework. Both lossy and lossless coding performances are better than those reported by A. Bilgin et al. (1998).
Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms

We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on context modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.
Temporal color video demosaicking via motion estimation and optimal data fusing

Demosaicking of the color CCD sensor data is an important task in digital image/video acquisition. Due to the Nyquist frequency limit of color filter array (CFA), it is impossible to faithfully reconstruct some high frequency structures in the original scene if one is limited to the mosaic samples of a still frame. However, a color digital video camera captures a sequence of mosaic images and the temporal dimension provides a wealth of information about the scene via camera and object motions. With the help of adjacent CCD frames by motion estimation and statistical data fusion, we can significantly improve the demosaicking quality of the video sequence. This paper presents a general approach of temporal demosaicking. Our experimental results demonstrate clear advantages of the presented temporal color demosaicking approach over its intra-frame counterparts both in PSNR measure and subjective visual quality.
Wavelet coding of volumetric medical images with high throughput and operability

In this paper we propose a new 3D wavelet-based scalable lossless coding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectiveness of the system, we strive to achieve high decoder throughput, random access to coded data volume, progressive transmission, and high compression ratio in a balanced design approach. These desirable functionalities are realized by a modified 3D wavelet transform tailored to volumetric medical images.
High-Fidelity Image Compression for High-Throughput and Energy-Efficient Cameras

We propose a new encoder-friendly image compression strategy for high-throughput cameras and other scenarios of resource-constrained encoders. The encoder performs $\ell_infty$-constrained predictive coding (DPCM coupled with uniform scalar quantizer), while the decoder solves an inverse problem of $\ell_2$ restoration of $\ell_\infty$-coded images. Although designed for minimum encoder complexity, the new codec outperforms the state-of-the-art encoder-centralized image codecs such as JPEG 2000 in PSNR for bit rates higher than 1.2 bpp, while maintaining much tighter $\ell_\infty$ error bounds as well. This is achieved through exploiting the tight error bound on each pixel naturally offered by the $\ell_\infty$-constrained encoder and by locally adaptive image modeling.
A study on quantization effects of DCT based compression

Quantization in discrete cosine transform (DCT) domain is a widely used lossy compression technique in international multimedia compression standards from audio (e.g., MP3) to image (e.g., JPEG) to video (e.g., H.264). Unlike many degradation sources, like sensor noises, quantization errors of DCT coefficients are signal dependent and difficult to isolate and remove, causing serious artifacts in decompressed and post-processed signals. In this research, quantization errors in the DCT domain are analytically assessed. Our analysis exposes complex behaviors of the DCT quantization errors after being mapped back into the temporal or spatial domain. These behaviors are highly sensitive to quantization precision, the amplitude and phase of the input signal. Based on these observations, we develop a DCT-domain error model to predict and quantify the quantization effects in cases where artifacts are most perceivable to humans, and offer some insights into possible strategies for further suppressing compression noises.
On the complexity of joint source-channel decoding of markov sequences over memoryless channels

We investigate the complexity of joint source-channel maximum a posteriori (MAP) decoding of a Markov sequence which is first encoded by a source code, then encoded by a convolutional code, and sent through a noisy memoryless channel. As established previously the MAP decoding can be performed by a Viterbi-like algorithm on a trellis whose states are triples of the states of the Markov source, the source coder and convolutional coder. The large size of the product space (in the order of K2N, where K is the number of source symbols and N is the number of states of the convolutional coder) appears to prohibit such a scheme. We show that in the case of finite impulse response convolutional codes the state space size can be reduced to O(K2 + NlogN), hence the decoding time becomes O(TK2 + TNlogN), where T is the length in bits of the decoded bitstream. We further show that an additional complexity reduction can be achieved when K > N, if the source satisfies a certain property, which is the case for a scalar quantized Gaussian-Markov source. This decrease becomes more significant as the tree structure of the source code is more unbalanced. The reduction factor ranges between O(K/N) (for a fixed-length source code) and O(K/logN) (for Golomb-Rice code)
A tree-structured locally optimal vector quantizer

A tree-structured VQ (vector quantizer) that performs the nearest-neighbor encoding based on a locally optimal codebook generated by the algorithm of Y. Linde, A. Buzo and R.M. Gray (1980) is proposed. A design method is given to organize the code words by a quasi-voronoi tree. This tree structure allows the nearest-neighbor encoding without an exhaustive search. For a codebook of size K, encoding an input vector takes an expected number of O(log K) distortion evaluations for dimensionalities below eight; that time complexity is O(K/sup 1/2/) in practice for higher dimensionalities. The tree-structured VQ achieves a good compromise between optimality and encoding speed.<<ETX>>
GPU-aided directional image/video interpolation for real time resolution upconversion

Image/video spatial resolution upconversion aims to obtain a high resolution output from the original low resolution input. Fast resolution upconversion is desired in many applications. In this paper, we develop a GPU-friendly two-pass directional image/video resolution upconversion algorithm and present a GPU implementation of the method, using the NVIDIA CUDA (Compute Unified Device Architecture) technology. Design considerations to speed up the execution are discussed, by taking full advantage of the properties of the CUDA framework and the upconversion scheme. Experimental results show that using a mid-range GPU card, the GPU-optimized resolution upconversion implementation can be more than five times as fast as the original method.
Minimum conditional entropy context quantization

We consider the problem of finding the quantizer Q that quantizes the K-dimensional causal context C/sub i/=(X/sub i-t(i)/,X/sub i-t(2)/,...,X/sub i-t(K)/) of a source symbol X/sub i/ into one of M conditioning states such that the conditional entropy H(X/sub i/|Q(C/sub i/)) is minimized. The resulting minimum conditional entropy context quantizer can be used for sequential coding of the sequence X/sub 0/, X/sub 1/, X/sub 2/,.
On $L_{\infty}$  Properties of Multiresolution Scalar Quantizers

We investigate the max-norm (L∞) properties of multiresolution scalar quantizers (MRSQ). The multiresolution requirement imposes nontrivial constraints on the maximum distortion at each level of the quantizer. To quantify these constraints, we define the overall multiresolution L∞ distortion of an MRSQ to be a weighted sum of L∞ distortions over all refinement levels of the MRSQ. We then seek MRSQ constructions that minimize this average-max distortion measure. An interesting relationship between this problem and the structure of Huffman code trees is established. Lower bounds for the average-max distortion are derived based on this relationship. The derivation of these lower bounds also lead to efficient dynamic programming heuristic solutions.
On Rate-Distortion Models for Natural Images and Wavelet Coding Performance

Operational rate-distortion (RD) functions of most natural images, when compressed with state-of-the-art wavelet coders, exhibit a power-law behavior DpropR-gamma at moderately high rates, with gamma being a constant depending on the input image, deviating from the well-known exponential form of the RD function Dprop2-xiR for bandlimited stationary processes. This paper explains this intriguing observation by investigating theoretical and operational RD behavior of natural images. We take as our source model the fractional Brownian motion (fBm), which is often used to model nonstationary behaviors in natural images. We first establish that the theoretical RD function of the fBm process (both in 1-D and 2-D) indeed follows a power law. Then we derive operational RD function of the fBm process when wavelet encoded based on water-filling principle. Interestingly, both the operational and theoretical RD functions behave as DpropR-gamma. For natural images, the values of gamma are found to be distributed around 1. These results lend an information theoretical support to the merit of multiresolution wavelet compression of self-similar processes and, in particular, natural images that can be modelled by such processes. They may also prove useful in predicting performance of RD optimized image coders
On the complexity of joint source-channel decoding of Markov sequences over memoryless channels

We investigate the complexity of joint source- channel maximum a posteriori (MAP) decoding of a Markov sequence which is first encoded by a source code, then encoded by a convolutional code, and sent through a noisy memoryless channel. As established previously the MAP decoding can be performed by a Viterbi-like algorithm on a trellis whose states are triples of the states of the Markov source, source coder and convolutional coder. The large size of the product space (in the order of K2N, where K is the number of source symbols and N is the number of states of the convolutional coder) appears to prohibit such a scheme. We show that for finite impulse response convolutional codes, the state space size of joint source-channel decoding can be reduced to O(K2+N log N), hence the decoding time becomes O(TK2 +TN log N), where T is the length in bits of the decoded bitstream. We further prove that an additional complexity reduction can be achieved when K > N, if the logarithm of the source transition probabilities satisfy the so- called Monge property. This decrease becomes more significant as the tree structure of the source code is more unbalanced. The reduction factor ranges between O(K/N) (for a fixed-length source code) and O(K / log N) (for Golomb-Rice code).
Steganalysis of halftone images

We present a novel steganalysis technique for halftone images without knowledge of the original cover image. We first convert halftone images into grayscale-like images by low-pass filtering. The low-pass-filtered image is then decomposed using quadrature mirror filters, and a set of subband coefficients are generated at different scales and orientations. Next, a set of statistical features are computed from the subband coefficients and their predicated errors. Using Fisher linear discriminant analysis, a statistical classifier is designed to detect marked images. Experimental results demonstrate the effectiveness and accuracy of the proposed technique.
Fast length-constrained MAP decoding of variable length coded Markov sequences over noisy channel

The problem of maximum a posterior probability (MAP) decoding of a Markov sequence that is variable length coded and transmitted over a binary symmetric channel (BSC) is considered. The number of source symbols in the sequence, if made known to the decoder, can improve MAP decoding performance. But adding a sequence length constraint to MAP decoding problem increases its complexity and also converts the length-constrained MAP decoding problem into one of maximum-weight k-link path in a weighted directed acyclic graph. The corresponding graph optimization problem can be solved by a fast parameterized search algorithm that finds either the exact solution with high probability or a good approximate solution otherwise. The proposed algorithm has lower complexity and superior performance than the previous heuristic algorithms.
Linfinity constrained high-fidelity image compression via adaptive context modeling

In this paper, we study high-fidelity image compression with a given tight L(infinity) bound. We propose some practical adaptive context modeling techniques to correct prediction biases caused by quantizing prediction residues, a problem common to the existing DPCM-type predictive near-lossless image coders. By incorporating the proposed techniques into the near-lossless version of CALIC that is considered by many as the state-of-the-art algorithm, we were able to increase its PSNR by 1 dB or more and/or reduce its bit rate by 10% or more, more encouragingly, at bit rates around 1.25 bpp or higher, our method obtained competitive PSNR results against the best L(2)-based wavelet coders, while obtaining much smaller L(infinity) bound.
YIQ vector quantization in a new color palette architecture

Many CRT displays use color palettes to save highspeed frame buffer memory and to support many interactive, real-time graphics and imaging operations. Existing quantization algorithms for color palettes cluster colors as 3-D vectors in a color space. Thus, they fail to remove the statistical redundancy in the image space. This weakness prevents a more efficient use of frame buffer capacity. We propose a simple product vector quantization (VQ) technique for frame buffers that exploits redundancies in both image and color spaces. The new VQ technique can reduce the frame buffer use of a color image by half from the current algorithms with comparable image quality. The NTSC YIQ rather than RGB color space is used in our product VQ scheme. Consequently, we propose to modify the current architecture of the color palette to suit the proposed product VQ algorithm in the YIQ mode. The modified color palette is nearly ten times smaller than the current RGB palette and is more flexible. The improved space efficiency in both frame buffer and color palette is achieved while neither complicating the control or logic of the frame buffer architecture nor increasing computational complexity of color quantization.
High-order context modeling and embedded conditional entropy coding of wavelet coefficients for image compression

Much of the progress in wavelet image compression came from better context modeling and entropy coding of quantized wavelet coefficients. In the past few months many papers on the subject were published. They reported rate-distortion performance results that are the best or near the best in the literature. Given seemingly ever smaller improvements on R-D performance with increasing sophistication and complexity of current R-D optimized quantizers and context models of wavelet coefficients, two tantalizing questions are whether there still exists some room for even higher coding efficiency of wavelet image coders, and if so, whether the improvement can be made with an embedded code stream. This paper sheds some lights on, and offers modestly encouraging answers to these questions.
Joint Color Decrosstalk and Demosaicking for CFA Cameras

In interest of low cost, low power consumption, and compact size, most digital cameras adopt a design of single sensor array coupled with a color filter array. This design inevitably suffers, due to physical characteristics of the optical and semiconductor components and the imperfection of manufacturing, from the problem of crosstalk between different color channels. Channel crosstalk can desaturate colors and blur image details, but the problem was seemingly overlooked by existing color demosaicking algorithms. To rectify this deficiency we propose a new joint demosaicking and decrosstalk technique that counters the effects of channel crosstalks by adaptive least-squares inverse filtering. The new technique integrates the operations of deconvolution for crosstalk removal and interpolation for color demosaicking, and it introduces a general framework in which any spatially varying crosstalks and varying spatial-spectral correlations can be modeled and factored into the color reproduction. Simulation results show that the proposed technique is highly effective and capable to obtain both high color fidelity and sharp, clean spatial details.