nils-herrmann/clean_scirepeval huggingface dataset:
Towards Optimality in Multiterminal Transform Coding

This paper is concerned with transform coding of correlated sources in conjunction with variable rate quantization at high resolution. The approach builds on our prior work on optimality conditions for transform coding in the point-to-point setting. The first contribution involves transform coding with decoder side information. In this setting, side information is only available to the decoder, whereas the encoder knows the joint statistics. The necessary and sufficient condition for optimality of a unitary transform in the side information setting is derived, namely, such transform minimizes a conditional divergence-based measure of inter-dependence of the transform coefficients, given the side information. This optimality result subsumes prior, known results that were restricted to the Gaussian case, where the conditional Karhunen-Loeve transform is optimal. The second contribution involves distributed transform coding, where two correlated sources are to be transform coded separately, but decoded jointly. The necessary and sufficient condition for optimality of unitary transforms in the distributed coding setting is derived. It is then specialized to produce closed form optimal transforms for specific source densities, including the case of jointly Gaussian sources.
Frequency domain singular value decomposition for efficient spatial audio coding

Advances in virtual reality have generated substantial interest in accurately reproducing and storing spatial audio in the higher order ambisonics (HOA) representation, given its rendering flexibility. Recent standardization for HOA compression adopted a framework wherein HOA data are decomposed into principal components that are then encoded by standard audio coding, i.e., frequency domain quantization and entropy coding to exploit psychoacoustic redundancy. A noted shortcoming of this approach is the occasional mismatch in principal components across blocks, and the resulting suboptimal transitions in the data fed to the audio coder. Instead, we propose a framework where singular value decomposition (SVD) is performed after transformation to the frequency domain via the modified discrete cosine transform (MDCT). This framework not only ensures smooth transition across blocks, but also enables frequency dependent SVD for better energy compaction. Moreover, we introduce a novel noise substitution technique to compensate for suppressed ambient energy in discarded higher order ambisonics channels, which significantly enhances the perceptual quality of the reconstructed HOA signal. Objective and subjective evaluation results provide evidence for the effectiveness of the proposed framework in terms of both higher compression gains and better perceptual quality, compared to existing methods.
Optimizing Motion Compensated Prediction for Error Resilient Video Coding

This paper is concerned with optimization of the motion compensated prediction framework to improve the error resilience of video coding for transmission over lossy networks. First, accurate end-to-end distortion estimation is employed to optimize both motion estimation and prediction within an overall rate-distortion framework. Low complexity practical variants are proposed: a method to approximate the optimal motion via simple distortion and source coding rate models, and a source-channel prediction method that uses the expected decoder reference frame for prediction. Second, reference frame generation is revisited as a problem of filter design to optimize the error resilience versus coding efficiency tradeoff. The special cases of leaky prediction and weighted prediction (i.e., finite impulse response filtering), are analyzed. A novel reference frame generation approach, called ¿generalized source-channel prediction¿, is proposed, which involves infinite impulse response filtering. Experimental results show significant performance gains and substantiate the effectiveness of the proposed encoder optimization approaches.
A joint spatio-temporal filtering approach to efficient prediction in video compression

A novel filtering approach that naturally combines information from both intra-frame and motion compensated referencing for efficient prediction is proposed to fully exploit the spatio-temporal correlations of video signals, thereby achieving superior compression performance. Inspiration was drawn from our recent work on extrapolation filter based intra prediction, which views the spatial signal as a non-separable first-order Markov process and employs a 3-tap recursive filter to effectively capture the statistical characteristics. This work significantly extends the scope to further incorporate motion compensated reference in a filtering framework, whose coefficients were optimized via a “k-modes”-like iteration that accounts for various factors in the compression process including variation in statistics in the prediction loop, to minimize the rate-distortion cost. Experiments validate the efficacy of the proposed spatio-temporal approach, which translates into consistent coding performance gains.
Implementing a CMOS boundary-scan architecture tutorial

This tutorial discusses the implementation of a boundary-scan architecture in CMOS. The boundary-scan testability features and principles, as discussed in the Institute of Electrical and Electronics Engineers (IEEE) Standard 1149.1, are reviewed. Next, we describe the necessary steps to convert those principles into a practical boundary-scan architecture implementation. Our implementation was for the MOSIS 2 /spl mu/m Scalable CMOS (SCMOS) p-well process. Design tactics and issues for the individual architecture components are treated as well.<<ETX>>
A Nonconvex Cost Optimization Approach to Tracking Multiple Targets by a Parallel Computational Network

The problem of tracking multiple targets in the presence of displacement noise and clutter is formulated as a nonconvex optimization problem. The form of the suggested cost function is shown to be suitable for the Graduated Non-Convexity algorithm, which can be viewed as deterministic annealing. The method is first derived for the two-dimensional (spatial/ temporal) case, and then generalized to the multi-dimensional case. The complexity grows linearly with the number of targets. Computer simulations show the performance with crossing trajectories.
Combinatorial Message Sharing and a New Achievable Region for Multiple Descriptions

This paper presents a new achievable rate-distortion region for the general L channel multiple descriptions (MDs) problem. A well-known general region for this problem is due to Venkataramani, Kramer, and Goyal (VKG). Their encoding scheme is an extension of the El Gamal-Cover (EC) and Zhang-Berger (ZB) coding schemes to the L channel case and includes a combinatorial number of refinement codebooks, one for each subset of the descriptions. As in ZB, the scheme also allows for a single common codeword to be shared by all descriptions. This paper proposes a novel encoding technique involving combinatorial message sharing (CMS), where every subset of the descriptions may share a distinct common message. This introduces a combinatorial number of shared codebooks along with the refinement codebooks of. These shared codebooks provide a more flexible framework to tradeoff redundancy across the messages for resilience to descriptions loss. We derive an achievable rate-distortion region for the proposed technique, and show that it subsumes the VKG region for general sources and distortion measures. We further show that CMS provides a strict improvement of the achievable region for any source and distortion measures for which some two-description subset is such that ZB achieves points outside the EC region. We then show a more surprising result: CMS outperforms VKG for a general class of sources and distortion measures, including scenarios where the ZB and EC regions coincide for all two-description subsets. In particular, we show that CMS strictly improves on VKG, for the L-channel quadratic Gaussian MD problem, for all L ≥ 3, despite the fact that the EC region is complete for the corresponding two-descriptions problem. Consequently, the correlated quantization scheme (an extreme special case of VKG) that has been proven to be optimal for several cross sections of the L-channel quadratic Gaussian MD problem is strictly suboptimal in general. Using the encoding principles derived, we show that the CMS scheme achieves the complete rate-distortion region for several asymmetric cross sections of the L-channel quadratic Gaussian MD problem.
Distributed Predictive Coding for Spatio-Temporally Correlated Sources

Distributed coding of correlated sources with memory poses a number of considerable challenges that threaten its practical application, particularly (but not only) in the context of sensor networks. This problem is strongly motivated by the obvious observation that most common sources exhibit temporal correlations that may be at least as important as spatial or intersource correlations. This paper presents an analysis of the underlying tradeoffs, paradigms for coding systems, and approaches for distributed predictive coder design optimization. Motivated by practical limitations on both complexity and delay (especially for dense sensor networks) the focus here is on predictive coding. From the source coding perspective, the most basic tradeoff (and difficulty) is due to conflicts that arise between distributed coding and prediction, wherein ldquostandardrdquo distributed quantization of the prediction errors, if coupled with imposition of zero decoder drift, would drastically compromise the predictor performance and hence the ability to exploit temporal correlations. Another challenge arises from instabilities in the design of closed-loop predictors, whose impact has been observed in the past, but is greatly exacerbated in the case of distributed coding. In the distributed predictive coder design, we highlight the fundamental tradeoffs encountered within a more general paradigm where decoder drift is allowable or unavoidable, and must be effectively accounted for and controlled. We derive an overall design optimization method for distributed predictive coding that avoids the pitfalls of naive distributed predictive quantization and produces an optimized low complexity and low delay coding system. The proposed iterative algorithms for distributed predictive coding subsume traditional single-source predictive coding and memoryless distributed coding as extreme special cases.
A range searching engine for VLSI design CAE

The design of a VLSI (very-large-scale integration) chip for a range-searching engine is described. The chips are connected as identical nodes in a folded binary tree structure, and all leaf nodes are lined to a common data bus which reports the matched data to the host system. A software system for VLSI design that is based on the range-searching engine concepts has been built and tested. The results show that an O(R+k+log N) time complexity for a range-searching query is achieved.<<ETX>>
Optimal jamming Over additive noise: Vector source-channel case

This paper extends the recent analysis of optimal zero-delay jamming problem, specialized to scalar sources over scalar additive noise channels, to vector spaces. Particularly, we find the saddle point solution to the jamming problem in “vector spaces”, within its most general, non-Gaussian setting; the problem has been open even for the important special case of Gaussian source and channel. Similar to the scalar setting, linearity conditions for encoding and decoding mappings play a pivotal role in jamming, in the sense that the optimal jamming strategy is to effectively force both transmitter and receiver to default to linear mappings, i.e., the jammer ensures, whenever possible, that the transmitter and receiver cannot benefit from non-linear strategies. The optimal strategy is then “randomized” linear encoding for transmitter and generating independent noise for the jamming. Moreover, the optimal jammer allocates power according to the well-known “water-filling” over the eigenvalues of the channel noise, and the density of the jamming noise that render the optimal encoding and decoding mappings linear, is determined by the power constraints and the source density.
Robust image compression for transmission over time-varying channels

We propose a rate-distortion (RD) approach to the optimization of image coding for transmission over time-varying channels. The information available about the channel error rate, is used to adapt the joint source-channel coding (JSCC) scheme. We focus on structured JSCC schemes to obtain realizations with feasible complexity. The image is hierarchically quantized, followed by unequal channel error protection matched to the importance of the bits. We allow the use of variable length codes in the source coder which provides adaptation to image statistics and higher compression performance. At the same time, we maintain error resilience by sending the synchronization information error free (through heavy protection). We show that efficient rate-distortion performance can be achieved by optimizing the overall system for the target rate and channel conditions. We present simulation results obtained on coding images for time-varying channels, which show that RD optimized JSCC schemes substantially outperforms conventional fixed length JSCC and can gain more than 6 dB in PSNR of the reconstructed frames.
A 87 dB, 2.3 GHz, SiGe BiCMOS operational transconductance amplifier

This paper describes a 3.3 V, fully differential SiGe BiCMOS operational transconductance amplifier (OTA) for switched capacitor applications. The simulated DC gain of the OTA is 87 dB and its unity gain frequency is 2.3 GHz at a phase margin of 63 degrees. The OTA is optimally compensated for fast settling using a 0.6 pF capacitor, dissipates 17 mW power, has a differential output swing of 2.4 V and the differential input-referred noise PSD is 2.7 nV//spl radic/Hz. This OTA settles to 0.01% accuracy within 4.3 ns when configured in a closed loop with feedback factor /spl beta/ = 1/2 . The above specifications will allow realization of a 12-bit, 115 MS/s gain-of-2 sample and hold amplifier for use at the front end of a pipeline ADC. This design uses a BiCMOS technology that provides a 47 GHz f/sub t/ SiGe HBTs and 250 nm CMOS. Results are based on circuit level SpectreS simulations in Cadence, with foundry provided transistor models.
Drift management and adaptive bit rate allocation in scalable video coding

This paper is concerned with performance optimization of point-to-point communication of scalable video over lossy networks. A variant of the recursive optimal per-pixel estimate (ROPE) is developed and embedded within a rate-distortion (RD) optimized mode selection scheme for SNR scalable video coding. The system allows predicting the current base layer frame from past enhancement layer frame data. Drift management and adaptive bit rate allocation are both RD optimized within this framework. The ROPE method. ensures accurate estimation (at the encoder) of the over-all decoder distortion, which is critical to the substantial performance gains achieved. A low complexity layer sequential optimization scheme is proposed as well, which approximate!; the more complex joint optimization scheme, while maintaining most of the performance gains.
Transform-domain temporal prediction in video coding: Exploiting correlation variation across coefficients

Temporal prediction in standard video coding is performed in the spatial domain, where each pixel is predicted from a motion-compensated reconstructed pixel in a prior frame. This paper is premised on the realization that such standard prediction treats each pixel independently and ignores underlying spatial correlations, while transform-domain prediction would eliminate much of the spatial correlation before signal components (transform coefficients) are independently predicted. Moreover, the true temporal correlations emerge after signal decomposition, and vary considerably from low to high frequency components. This precise nature of the temporal dependencies is entirely masked in spatial domain prediction by the high temporal correlation coefficient (ρ ≈ 1) imposed on all pixels by the dominant low frequency components. We derive optimal transform-domain per-coefficient predictors for three main settings: basic inter-frame prediction; bi-directional prediction; and enhancement-layer prediction in scalable coding. Experimental results provide evidence for substantial performance gains in all settings.
Optimal mode switching for multi-hypothesis motion compensated prediction

Transmission of compressed video over unreliable networks is vulnerable to errors and error propagation. Multi-hypothesis motion compensated prediction (MHMCP) which was originally developed to improve compression efficiency has been shown to have a good error resilience property. In this paper we improve the overall performance of MHMCP in packet loss scenarios by performing optimal mode switching within a rate distortion framework. The approach builds on the recursive optimal per-pixel estimate (ROPE), which is extended by re-deriving recursion formulas for the more complex MHMCP setting, so as to achieve an accurate estimation of the end-to-end distortion. Simulation results show significant performance gains over the standard MHMCP scheme and the importance of performing effective mode decisions. We also show results with comparison to conventional ROPE.
Efficient scalable coding of stereophonic audio by conditional quantization and estimation-theoretic prediction

The standard scalable coding of stereophonic audio suffers from significant performance loss because of (1) poor prediction gain at the enhancement-layer and (2) direct requantization of the reconstruction error, which is suboptimal for the noise-mask ratio (NMR) criterion. To mitigate such performance loss, this paper proposes an integrated approach which employs two complementary techniques, namely, the estimation theoretic (ET) predictor and the conditional enhancement-layer quantizer (CELQ). The ET predictor has been shown to combine information from various sources for efficient enhancement-layer prediction, while CELQ efficiently handles scalable quantization to minimize NMR. We demonstrate that the proposed combined approach can achieve major performance gains in terms of bit rate reduction and reconstruction quality enhancement. For example, the proposed 2/spl times/16 kbit/s two layer coder achieves considerably improved reconstruction quality compared to that of the conventional 4/spl times/16 kbit/s four layer coder, despite expending only 50% of the standard scalable coder bit rate.
Code Design for Fast Selective Retrieval of Fusion Stored Sensor-Network/Time-Series Data

We consider the problem of storing data from multiple correlated sources in a database, so as to enable efficient future retrieval of data from select subsets of the sources, where only statistical information about queries may be available in advance. This setting poses new challenges in terms of the precise tradeoffs between storage rate, retrieval rate and distortion. We derive a gradient descent algorithm to optimize the joint encoding (storage) procedure, the decoder, and the retrieval procedure via mapping from queries to subsets of the stored data to retrieve and decode, so that the average retrieval rate-distortion cost is minimized, given a pre-specified overall storage capacity (or rate). Experiments conducted on real and synthetic data-sets demonstrate that our selective retrieval procedure is able to achieve significantly better trade-offs than joint compression, with retrieval speed-ups reaching 5X and distortion gains of up to 3.5 dB possible.
Recursive end-to-end distortion estimation with model-based cross-correlation approximation

Accurate end-to-end distortion estimation is critical to efficient rate-distortion (R-D) optimization of encoder decisions for video transmission over lossy packet networks. This work focuses on extensions of the recursive optimal per-pixel estimate (ROPE), which has been shown to provide accurate end-to-end distortion estimation. Of particular interest are difficulties due to sub-pixel prediction and other pixel averaging operations, for which the existing ROPE encounters cross-correlation terms, whose exact estimation requires prohibitive storage and computational complexity. In this paper, we propose two model-based methods, which approximate the cross-correlation of two pixels as a function of their available first and second marginal moments. This allows an approximate extension of ROPE to handle sub-pixel prediction and other pixel averaging operations, at no additional storage cost, and no significant additional complexity. Simulations provide evidence for the performance gains of the proposed methods, and in particular, demonstrate that the resulting accuracy is very close to that of ROPE when it is optimal, i.e., in the case of full pixel prediction.
A Unified Estimation-Theoretic Framework for Error-Resilient Scalable Video Coding

A novel scalable video coding (SVC) scheme is proposed for video transmission over loss networks, which builds on an estimation-theoretic (ET) framework for optimal prediction and error concealment, given all available information from both the current base layer and prior enhancement layer frames. It incorporates a recursive end-to-end distortion estimation technique, namely, the spectral coefficient-wise optimal recursive estimate (SCORE), which accounts for all ET operations and tracks the first and second moments of decoder reconstructed transform coefficients. The overall framework enables optimization of ET-SVC systems for transmission over lossy networks, while accounting for all relevant conditions including the effects of quantization, channel loss, concealment, and error propagation. It thus resolves longstanding difficulties in combining truly optimal prediction and concealment with optimal end-to-end distortion and error-resilient SVC coding decisions. Experiments demonstrate that the proposed scheme offers substantial performance gains over existing error-resilient SVC systems, under a wide range of packet loss and bit rates.
Sub-state tying in tied mixture hidden Markov models

An approach is proposed for partial tying of states of tied-mixture hidden Markov models. To facilitate tying at the sub-state level, the state emission probabilities are constructed in two stages, or equivalently, are viewed as a "mixture of mixtures of Gaussians." This paradigm allows, and is complemented with, an optimization technique to seek the best complexity-accuracy tradeoff solution, which jointly exploits Gaussian density sharing and sub-state tying. Experimental results on the E-set show that the classification error rate is reduced by over 20% compared to standard Gaussian sharing and whole-state tying. The approach is then embedded within the recently developed procedure of combined parameter training and reduction technique. Experiments with the overall technique show that the error rate is further reduced by 8%.