nils-herrmann/clean_scirepeval huggingface dataset:
Jointly optimized VQ index assignment and transmission energy allocation

In this work we address the problem of designing robust, vector quantizer (VQ) based communication systems for operation over time-varying Gaussian channels. Transmission energy allocation (TEA) to VQ codeword bits, according to their error sensitivities, is a powerful tool for improving robustness to channel noise. The power of this technique can be further enhanced by appropriately combining it with index assignment methods. We pose the corresponding joint optimization problem and suggest a simple iterative algorithm for finding a locally optimal solution. The susceptibility of the solution to poor local minima is significantly reduced by an enhanced version of the algorithm which invokes the the method of noisy channel relaxation, whereby, the VQ system is optimized while gradually decreasing the assumed level of channel noise. The resulting combined technique is shown to outperform standard pseudo-Gray coding by up to 3.5 dB, and to exhibit graceful degradation at mismatched channel conditions. We conclude with a brief discussion of the impact of TEA on the peak-to-average energy ratio of the transmitted modulated signals.
Adaptive interpolated motion compensated prediction

Current video coders rely heavily on block-based motion compensation, which is known to accurately capture pure translation, but to (at best) approximate all other types of motion, such as rotation and zoom. Moreover, as motion vectors are obtained through pixel-domain block matching to optimize a rate-distortion cost, and do not necessarily represent the actual motion, the model should not be considered a proper sampling of the underlying pixel motion field. This paper explicitly treats several neighboring motion vectors as pointers to multiple observation sources for estimating a pixel in the current frame. The corresponding optimal linear estimation coefficients are derived for predicting each pixel, given the observations obtained based on nearby motion vectors. Prediction coefficients are further adapted to local statistics by switching between predefined sets of coefficients, which are trained offline through a procedure of “K-modes” clustering. Experimental results outside the training set validate this paradigm with significant bit rate savings over conventional motion compensated prediction.
An estimation-theoretic approach to spatially scalable video coding

This paper focuses on prediction optimality in spatially scalable video coding. It is inspired by the earlier estimation-theoretic prediction framework developed by our group for quality (SNR) scalability, which achieved optimality by fully accounting for relevant information from the current base layer (e.g., quantization intervals) and the enhancement layer, to efficiently calculate the conditional expectation that forms the optimal predictor. It was central to that approach that all layers reconstruct approximations to the same original transform coefficient. In spatial scalability, however, the layers encode different resolution versions of the signal. To approach optimality in enhancement layer prediction, the current work departs from existing spatially scalable codecs that employ pixel-domain resampling to perform inter-layer prediction. Instead, it incorporates a transform-domain resampling technique that ensures that the base layer quantization intervals are accessible and usable at the enhancement layer, which in conjunction with prior enhancement layer information, enable optimal prediction. Simulations provide experimental evidence that the proposed approach achieves substantial enhancement layer coding gains over the standard.
Memory performance prediction for high-performance microprocessors at deep submicrometer technologies

The desire for large size, high-speed, and low-power on-chip memory necessitates early and accurate estimates of memory performance. A new performance model as well as an early cache design tool and predictor of access and cycle time for cache stack (PRACTICS) has been developed for on-chip static random access memory (SRAM) cache design that includes both delay and dynamic-power models. Efficient models for distributed interconnect delays, verified by Cadence simulations, are introduced, and their necessity is demonstrated. In the delay model, the access time is estimated by decomposing each component into several equivalent lumped resistance-capacitance (RC) circuits and using an appropriate order pi model to approximate the distributed wire delays of each stage. The dynamic-power model calculates the charging power dissipation of the load capacitances using the same equivalent lumped RC circuits. The delay model has been validated with an Intel 18-Mb SRAM at the 180-nm node, achieving accuracy to within 10% of the measured results. The dynamic-power model has been validated with an International Business Machines Corporation (IBM) 18-Mb SRAM at the 180-nm node, to within 13% of the measured power consumption. Detailed comparisons between PRACTICS and cache access and cycle time model (CACTI) in both validation cases indicate that an improved wire delay, appropriate circuit structures, and technology dependent parameters are necessary to accurately predict large cache memory performance at deep submicrometer technology nodes. PRACTICS is used to analyze the access time and power consumption in terms of cache sizes and various degrees of associativity for architectural studies. In addition, the PRACTICS simulation results show that repeater insertion reduces the access time significantly, with a small overhead in dynamic-power consumption for large size cache design at deep submicrometer technology
Optimization of zero-delay mappings for distributed coding by deterministic annealing

This paper studies the optimization of zero-delay analog mappings in a network setting that involves distributed coding. The cost surface is known to be non-convex, and known greedy methods tend to get trapped in poor locally optimal solutions that depend heavily on initialization. We derive an optimization algorithm based on the principles of “deterministic annealing”, a powerful global optimization framework that has been successfully employed in several disciplines, including, in our recent work, to a simple zero-delay analog communications problem. We demonstrate strict superiority over the descent based methods, as well as present example mappings whose properties lend insights on the workings of the solution and relations with digital distributed coding.
Locally optimal codebook design for quadtree-based vector quantization

The optimal design of quadtree-based vector quantizers is addressed. Until now, work in this area has focused on optimizing the quadtree structure for a given set of leaf quantizers with little attention spent on the design of the quantizers themselves. In cases where the leaf quantizers were considered, codebooks were optimized without regard to the ultimate quadtree segmentation. However, it is not sufficient to consider each problem independently, as separate optimization leads to an overall suboptimal solution. Rather, joint design of the quadtree structure and the leaf codebooks must be considered for overall optimality. The method we suggest is a "quadtree" constrained version of the entropy-constrained vector quantization design method. To this end, a centroid condition for the leaf codebooks is derived that represents a necessary optimality condition for variable-rate quadtree coding. This condition, when iterated with the optimal quadtree segmentation strategy of Sullivan and Baker (see  May 1994) results in a monotonically descending rate-distortion cost function, and consequently, an (at least locally) optimal quadtree solution.
Optimal intra/inter mode switching for robust video communication over the Internet

Robustness to packet loss is a critical requirement in video coding for transmission over the Internet. We propose an intra/inter mode switching scheme to stop error propagation, while optimizing the compression efficiency. The encoder computes the expected distortion at the decoder precisely per pixel, while accounting for quantization, packet loss rate, and effect of error concealment. The distortion estimate is incorporated within an overall rate-distortion framework to optimally select the coding mode of each macroblock. Simulation results demonstrate the accuracy of the estimate, and show that substantial gains in PSNR can be achieved over state-of-the-art RD and non-RD based mode-switching approaches for transmission over networks with packet loss.
Nearest-prototype classifier design by deterministic annealing with random class labels

The design of nearest-prototype (NP) classifiers is a challenging problem because of the prevalence of poor local minima, and the piecewise constant nature of the cost function which is incompatible with gradient-based techniques. The paper extends the deterministic annealing (DA) method for NP-classifier design in two ways. First, the association between prototypes and class labels is also randomized, and the corresponding association probabilities are added to the set of parameters to be optimized. Second, the multiplicity (or the mass) of prototypes are optimized. During the design, all parameters are optimized so as to minimize the expected misclassification rate for a given level of randomness. The joint entropy, which measures the level of randomness, is gradually reduced while optimizing the cost Lagrangian. As the entropy approaches zero, the method seeks a deterministic classifier that minimizes the rate of misclassification.
Minimum redundancy zero-error source coding with side information

We consider the design of instantaneous variable-length zero-error codes where the decoder has access to side information about the source, which is not available to the encoder. We show that finding the optimal code, which minimizes the expected codeword length, is an NP-hard problem. We derive an exponential-time optimal design algorithm. We also provide polynomial-time approximate algorithms, and discuss their average-case and worst-case performance guarantees.
Comparing models for the growth of silicon-rich oxides (SRO)

The relative advantages of several methods for modeling the growth of Silicon-Rich Oxide (SRO) films are compared. The methods are a response surface model, a physical model based on chemical kinetics, and neural network models. The physical model provides more insight and greater predictive ability. Neural network models provide better fits to complex response surfaces with minimal data and can be used successfully in the absence of a theoretical model. The risks of prediction by neural networks outside their training domain are demonstrated.
First-order performance prediction of cache memory with wafer-level 3D integration

The advantages of 3D design can be exploited by reducing the memory access time. In this article, the authors use a simulator based on analytical models to build an optimal processor-memory configuration for two designs: a graphics processor and a microprocessor. One emerging alternative approach to relieving these interconnect constraints is the use of wafer-level 3D integration, which provides a high density of high-performance, low-parasitic vertical interconnects. A wafer-level 3D design is partitionable into multiple chips connected by short vertical vias. This arrangement reduces the length of many global interconnects without introducing any logic complexity. Wafer-level 3D integration also reduces the required number of repeaters, thereby improving the area efficiency and reducing the power consumed within the interconnect network. With micron-size interwafer vias, wafer-level 3D integration allows a large memory bandwidth with little wafer area consumption. We have developed a software program that allows a first-order comparison of cache designs in 2D and 3D IC technologies. We present a first-order estimate of the performance improvements achieved by 3D implementation of cache memory, with emphasis on large caches in deep-submicron technologies.
Towards optimal clustering for approximate similarity searching

We propose an iterative optimization algorithm for the generic class of clustering-based indexing for approximate similarity searching. It was previously shown that clustering is a powerful component of approximate searching that reduces the number of retrieved data points. The objective of the proposed algorithm is to maximize the expected search quality given the query distribution. The problem is decomposed into minimization over three mapping functions, and fixed-point iterations of the algorithm alternately optimizing one mapping while fixing the other two. We demonstrate via experiments on real high dimensional data sets that the algorithm significantly improves the time/accuracy efficiency over heuristic clustering design.
A perceptually enhanced Scalable-to-Lossless audio coding scheme and a trellis-based approach for its optimization

Scalable-to-Lossless (SLS) audio compression, as standardized by MPEG, provides a lossy base layer compatible with the Advanced Audio Coding (AAC) format, ensuring state-of-the-art quality in the base layer, and additional fine grained enhancements that eventually provide a lossless compressed version of the signal. While SLS offers highly efficient lossless compression, the perceptual quality of its intermediate lossy layers has been observed to be suboptimal. This paper proposes a modified SLS audio coding scheme that provides enhanced perceptual quality at an intermediate bit-rate, at the expense of an additional parameter per frequency band as side-information. This scheme when coupled with a trellis-based optimization algorithm is demonstrated to outperform, in terms of quality at the intermediate bit-rate, both standard SLS and a recent perceptually enhanced variant, with minimal degradation in lossless coding performance.
DCT/DST alternate-transform image coding

An image coding method for low bit rates is proposed. It is based on alternate use of the discrete cosine transform (DCT) and the discrete sine transform (DST) on image blocks. This procedure achieves the removal of redundancies in the correlation between neighboring blocks as well as the preservation of continuity across the block boundaries. An outline of the mathematical justification of the method, assuming a certain first-order Gauss-Markov model, is given. The resulting coding method is then adapted to nonstationary real images by locally adapting the model parameters and improving the block classification technique. Simulation results are shown and compared with the performance of related previous methods, namely adaptive DCT and fast Karhunen-Loeve transform (FKLT).
Natural type selection in adaptive lossy compression

Consider approximate (lossy) matching of a source string /spl sim/P, with a random codebook generated from reproduction distribution Q, at a specified distortion d. Previous work determined the minimum coding rate R/sub 1/=R(P, Q, d) for this setting. We observe that for a large word length and with high probability, the matching codeword is typical with a distribution Q/sub 1/ which is different from Q. If a new random codebook is generated /spl sim/Q/sub 1/, then the source string will favor codewords which are typical with a new distribution Q/sub 2/, resulting in a minimum coding rate R/sub 2/=R(P, Q/sub 1/, d), and so on. We show that the sequences of distributions Q/sub 1/, Q/sub 2/,... and rates R/sub 1/, R/sub 2/,..., generated by this procedure, converge to an optimum reproduction distribution Q*, and the rate-distortion function R(P, d), respectively. We also derive a fixed rate-distortion slope version of this natural type selection process. In the latter case, an iteration of the process stochastically simulates an iteration of the Blahut-Arimoto (1972) algorithm for rate-distortion function computation (without recourse to prior knowledge of the underlying source distribution). To strengthen these limit statements, we also characterize the steady-state error of these procedures when iterating at a finite string length. Implications of the main results provide fresh insights into the workings of lossy variants of the Lempel-Ziv algorithm for adaptive compression.
Multimode video coding for noisy channels

We focus on the design of robust video compression schemes for transmission over noisy channels. A multimode video coding framework is introduced, which enables optimizing the tradeoff between the conflicting objectives of high compression efficiency and error resilience. The starting point is a simple block-based predictive coder, with no motion compensation, for which we develop the robust multimode video compression scheme. The proposed iterative design algorithm for multimode video coders directly minimizes the overall rate-distortion cost. We show that several conventional joint source-channel coding mechanisms can be incorporated within a multimode scheme to further enhance the video coder performance. Simulation results of compressing benchmark video sequences for transmission over noisy channel conditions are presented. They demonstrate that multimode coders outperform conventional fixed length approaches and can achieve substantial gains of more than 6 dB in PSNR of the reconstructed picture.
Towards Optimal Indexing for Relevance Feedback in Large Image Databases$^+$

Motivated by the need to efficiently leverage user relevance feedback in content-based retrieval from image databases, we propose a fast, clustering-based indexing technique for exact nearest-neighbor search that adapts to the Mahalanobis distance with a varying weight matrix. We derive a basic property of point-to-hyperplane Mahalanobis distance, which enables efficient recalculation of such distances as the Mahalanobis weight matrix is varied. This property is exploited to recalculate bounds on query-cluster distances via projection on known separating hyperplanes (available from the underlying clustering procedure), to effectively eliminate noncompetitive clusters from the search and to retrieve clusters in increasing order of (the appropriate) distance from the query. We compare performance with an existing variant of VA-File indexing designed for relevance feedback, and observe considerable gains.
Advances in Recursive Per-Pixel End-to-End Distortion Estimation for Robust Video Coding in H.264/AVC

This paper is focused on expanding the applicability of the recursive optimal per-pixel estimate (ROPE) of end-to-end distortion, to the H.264/AVC standard. One open question involves the complexity of cross-correlation terms that appear in the case of subpixel prediction and other pixel filtering operations. Several efficient model-based solutions are proposed. Another open question involves the largely ignored effects of rounding operations, whose cumulative impact may seriously degrade the estimate accuracy. Two approaches are proposed for rounding error compensation: one appeals to the maximum entropy principle, while a low complexity alternative is based on quantization theoretic approximations. The former effectively estimates the distribution of the decoder reconstruction, thereby significantly broadening the applicability of ROPE to all additive distortion measures. Simulation results for H.264/AVC with 1/4-pel prediction show that the proposed ROPE extensions achieve fairly high estimation accuracy, while maintaining low complexity. Another set of results demonstrates the level of overall coding gains achievable by exploiting such improved end-to-end distortion estimation.
Methods for Supplying Interactive Services on Broad-Band Communication Networks

The features and limitations of time- and frequencydivision miltiplexed (TDM and FDM) systems for providing interactive services on broad-band communication networks are considered. We find that TDM systems make efficient use of bandwidth for digital signal rates below 10 kbits/s while FDM systems are efficient for analog signals and digital signal rates above 1 kbit/s. Although TDM and FDM systems can employ static allocation in which fixed channels are assigned to subscribers, dynamic allocation increases the efficiency of bandwidth usage by an order of magnitude. A TDM interrupt scheme appears to be the most efficient way to identify requests and process control signals in a dynamic allocation system, but introduces a problem of interference between simultaneous signals (not present with a polling technique for request identification).
Perceptual zerotrees for scalable wavelet coding of wideband audio

This paper introduces a new algorithm for scalable coding of wideband audio signals. The technique is based on quantization of bi-orthogonal wavelet transformed coefficients using a perceptual zerotree method. An initial zerotree estimate of the wavelet coefficients is computed, followed by scalar quantization of the coefficients according to perceptual thresholds. The choice of wavelet decomposition and encoding parameters for each frame is adapted to the source characteristics employing a rate distortion criterion. The scalability of the coder is due to the tree structure, which enables graceful degradation with decrease in bit rate. Preliminary subjective tests indicate near-transparent quality for average bit rates in the range of 1.5 to 2.5 bits per sample.
Predictive vector quantizer design by deterministic annealing

Predictive vector quantizers (PVQs) are useful for the compression of sources with memory and other correlated data by incorporating memory into the quantization process. A new approach is proposed for the design of PVQs. By formulating both the prediction and quantization in probabilistic terms, a joint optimization procedure is made possible, which directly minimizes the expected distortion. The approach resolves two longstanding fundamental shortcomings of standard PVQ design. The first shortcoming is due to the piecewise constant nature of the quantizer function; it is difficult to optimize the predictor with respect to the overall reconstruction error. The second complication is due to the PVQ prediction loop, which has a detrimental impact on the convergence and the stability of the design procedure. We propose a new PVQ design approach, DA-ACL, that embeds the Khalil and Rose (2001) asymptotically closed-loop (ACL) approach within a deterministic annealing (DA) framework. The overall DA-ACL method profits from its two main components in a complementary way. DA eliminates the first design shortcoming by offering two benefits: its probabilistic framework replaces hard quantization with a differentiable expected cost function that can be jointly optimized for the predictor and quantizer parameters; and its annealing schedule allows avoiding many poor local optima. ACL is used to overcome the second difficulty and offers the means for stable quantizer design as it provides an open-loop design platform, yet allows the PVQ design algorithm to asymptotically converge to the objective closed-loop performance.