An upper bound on the entropy of run-length coding (Corresp.)

An upper bound on the entropy per run in binary run-length coding isa loga - (a - 1)log (a - 1), whereais the average run length. This upper bound is attained by a time-quantized Poisson square wave.     

A tight upper bound on discrete entropy

The standard upper bound on discrete entropy was derived based on the differential entropy bound for continuous random variables. A tighter discrete entropy bound is derived using the transformation formula of Jacobi theta function. The new bound is applicable only when the probability mass function of the discrete random variable satisfies certain conditions. Its application to the class of binomial random variables is presented as an example     

一种基于改进型游程编码的FPGA动态重构方法

在分析传统FPGA动态重构方法性能缺陷的基础上,创新性的提出了基于改进型游程编码的FPGA动态重构方法,并详细介绍了该方法的设计实现。与传统FPGA动态重构方法对比测试结果表明,基于改进型游程编码的FPGA动态重构方法不仅可以显著提高FPGA动态重构的速度,而且可以降低对程序存储器容量要求。目前,该技术已在重大工程项目中得到应用。     

Context conditioning and run-length coding for hybrid, embeddedprogressive image coding

An analysis of spatial context conditioning and run-length coding in embedded progressive, wavelet-based image coding is presented. The analysis shows that run-length coding of certain context subsequences is superior to pure entropy coding, both in terms of coding performance and of execution speed. Based on these considerations, a novel, intuitive context conditioning scheme using a spatial distance model to describe the statistics of significant coefficients is proposed. The results for the proposed coding scheme are competitive to the best coding schemes found in the literature.     

Conditional run-length coding for multilevel graphic image

The concept of conditional run-length coding mainly used for bilevel image is extended for multilevel graphic image. It is shown that the extended coding algorithm is also optimum for the Markov model. Its performance is compared with bit-plane coding and cosine transform coding.     

An enhanced entropy coding scheme for HEVC

This paper presents a modification to Context-based Adaptive Binary Arithmetic Coding (CABAC) in High Efficiency Video Coding (HEVC), which includes an improved context modeling for transform coefficient levels and a binary arithmetic coding (BAC) engine with low memory requirement. In the improved context modeling for transform coefficient levels, the context model index for significance map is dependent on the number of the significant neighbors covered by a local template and its position within transform block (TB). To limit the total number of context models for significance map, TBs are split into different regions based on the coefficient position. The same region in different TBs shares the same context model set. For the first and second bins of the truncated unary scheme of absolute level minus one, their context model indices depend on the neighbors covered by a local template of the current transform coefficient level. Specifically, the context model index for the first bin is determined by the number of neighbors covered by the local template with absolute magnitude equal to 1 and larger than 1; for the second bin, its context model index is determined by the number of neighbors covered by the local template with absolute magnitude larger than 1 and larger than 2. Moreover, TB is also split into different regions to incorporate the coefficient position in the context modeling of the first bin in luma component. In the BAC engine with low memory requirement, the probability is estimated based on a multi-parameter probability update mechanism, in which the probability is updated with two different adaption speeds and use the average as the estimated probability for the next symbol. Moreover, a multiplication with low bit capacities is used in the coding interval subdivision to substitute the large look-up table to reduce its memory consumption. According to the experiments conducted on HM14.0 under HEVC main profile, the improved context modeling for transform coefficient levels achieves 0.8%, 0.6% and 0.4% bitrate reduction on average for all intra (AI), random access (RA) and low delay (LD) configurations, respectively; the BAC engine with low memory requirement achieves 0.7%, 0.6% and 0.5% bitrate reduction on average for AI, RA and LD configurations, respectively; the overall bitrate reduction achieved by the proposed two techniques is 1.4%, 1.1% and 0.9% on average for AI, RA and LD configurations, respectively.     

An upper bound on the zero-error list-coding capacity

Presents an upper bound on the zero-error list-coding capacity of discrete memoryless channels. Using this bound, we show that the list-3 capacity of the 4/3 channel is at most 0.3512 bits, improving the best previous bound. The relation of the bound to earlier similar bounds, in particular, to Korner's (1986) graph-entropy bound, is discussed     

An upper bound on the rate distortion function for source coding with partial side information at the decoder

An upper bound on the rate distortion function is obtained for source coding with partial side information at the decoder. Previous results were for complete side information, i.e. full knowledge ofY_{n}. below. A diagram given in the paper helps to describe the problem. The bound is given in     

An improved upper bound on the block coding error exponent for binary-input discrete memoryless channels (Corresp.)

The recent upper bounds on the minimum distance of binary codes given by McEliece, Rodemich, Rumsey, and Welch are shown to result in improved upper bounds on the block coding error exponent for binary-input memoryless channels.     

Fast variable run-length coding for embedded progressivewavelet-based image compression

Run-length coding has attracted much attention in wavelet-based image compression because of its simplicity and potentially low complexity. The main drawback is the inferior RD-performance compared to the state-of-the-art-coder SPIHT. In this paper, we concentrate on the embedded progressive run-length code of Tian and Wells (1996, 1998). We consider significance sequences drawn from the scan in the dominant pass. It turns out that self-similar curves for scanning the dominant pass increase the compression efficiency significantly. This is a consequence of the correlation of direct neighbors in the wavelet domain. This dependence can be better exploited by using groups of coefficients, similar to the SPIHT algorithm. This results in a new and very fast coding algorithm, which shows performance similar to the state-of-the-art coder SPIHT, but with lower complexity and small and fixed memory overhead.     

An upper bound on average estimation error in nonlinear systems

An upper bound is obtained on the probability density of the estimate of the parametermwhen a nonlinear functions(t, m)is transmitted over a channel that adds Gaussian noise, and maximum likelihood or maximum a posteriori estimation is used. If this bound is integrated with a loss function, an upper bound on the average error is obtained. Nonlinear (below threshold) effects are included. The problem is viewed in a Euclidean space. Evaluation of the probability density can be reduced to integrating the probability density of the observation over part of a hyperplane. By bounding the integrand, and using a larger part of the hyperplane, an upper bound is obtained. The resulting bound on mean-square error is quite close for the cases calculated.     

An upper bound estimate on classification error (Corresp.)

An upper bound estimate on the Bayes misclassification error is derived using a kernel function to estimate probability density functions. As a result of this bound, a relationship between a Euclidean distance and a probability of misclassification is indicated.     

An upper bound on the error probability in decision-feedback equalization

An upper bound on the error probability of a decision-feedback equalizer which takes into account the effect of error propagation is derived. The bound, which assumes independent data symbols and noise samples, is readily evaluated numerically for arbitrary tap gains and is valid for multilevel and nonequally likely data. One specific result for equally likely binary symbols is that if the worst case intersymbol interference when the firstJfeedback taps are Set to zero is less than the original signal voltage, then the error probability is multiplied by at most a factor of2^Jrelative to the error probability in the absence of decision errors at highS/Nratios. Numerical results are given for the special case of exponentially decreasing tap gains. These results demonstrate that the decision-feedback equalizer has a lower error probability than the linear zero-forcing equalizer when there is both a highS/Nratio and a fast roll-off of the feedback tap gains.     

An upper bound on the cutoff rate of sequential decoding

An upper bound is given on the cutoff rate of discrete memoryless channels. This upper bound, which coincides with a known lower bound, determines the cutoff rate, and settles a long-standing open problem     

An upper bound on the minimum distance of a convolutional code

An upper bound on the minimum distance of a linear convolutional code is given which reduces to the Plotkin bound for the block code case. It is shown that most linear convolutional codes have a minimum distance strictly less than their average distance. A table of the bound for several rates is given for binary codes as well as a comparison with the known optimum values for codes of block length2.     

An upper bound on turbo codes performance over quasi-static fading channels

This letter proposes an upper bound on the performance of turbo-codes over quasi-static fading channels. First an upper bound is derived for the case of a single-input single-output channel. The result is then extended to the case of a serial concatenation of a turbo-code and a space-time block code. Unlike a simple extension of the union bound, the derived upper bounds are shown to converge for all signal-to-noise ratios. Additionally the closed form upper bounds obtained confirm analytically that, unlike over additive white Gaussian noise channels, turbo-code performance does not improve by increasing frame length over quasi-static fading channels.     

An upper bound on the performance of a novel feedforward perceptronequalizer

The emulation of a nonadaptive, binary, decision-feedback equalizer, operating on a noiseless, finite impulse response channel, by a feedforward multilayer processor is considered. This feedforward perceptron equalizer comprises a triangular array of bandlimiting processing elements. The functional similarity between the two systems is exploited to obtain a tight upper bound on the probability of error as a function of the number of layers, using the theory of finite-state Markov processes     

An improved upper bound on the minimum distance of doubly-evenself-dual codes

We derive a new upper bound on the minimum distance d of doubly-even self-dual codes of length n. Asymptotically, for n growing, it gives lim_n→∞ sup d/n⩽(5-5^3/4)/10<0.165630, thus improving on the Mallows-Odlyzko-Sloane bound of 1/6 and our recent bound of 0.166315     

An upper bound on the estimation error in the threshold region

An upper bound on the estimation error in the threshold region (probability of threshold effect and mean-square error) is obtained for nonlinear pulse modulation systems. The problem is viewed in anN-dimensional Euclidean space. The space of all received signals is divided into two regions, corresponding to the two types of error: weak-noise approximation and threshold effect. The threshold region is geometrically upper bounded by a larger region, and the estimation error is obtained as a sum of incompleteGammafunctions. The resulting bound on the mean-square error was found to be quite close for the cases calculated. An extension of the method to PPM system is also presented.