Search

Dynamic compression methods continuously update the model of the underlying text file to be compressed according to the already processed part, assuming that such models accurately predict the distribution in the remaining part. Since this premise is not necessarily true, we suggest to update the model only selectively. We give empirical evidence that this hardly affects the compression efficiency, while it obviously may save processing time and facilitate the use of the compression scheme for cryptographic applications. [ABSTRACT FROM AUTHOR]

Traditional dynamic Huffman algorithms update the frequencies adaptively after every character, according to the assumption that better compression can be achieved when all previous characters are taken into account, justifying the slow processing time. This, however, turns the encoded file into an extremely vulnerable one in the case of even a single bit error. Since the above mentioned assumption is not necessarily true, we explore blockwise dynamic Huffman variants, where the Huffman tree is periodically, rather than constantly, updated. Experiments show that avoiding the updates at every character and choosing larger blocks does not hurt the compression performance, and may even improve it at times. Moreover, the new scheme seems to be more robust against single errors introduced in the encoded file. [ABSTRACT FROM AUTHOR]

The surge in image data has significantly increased the pressure on storage and transmission, posing new challenges for image compression technology. The structural texture of an image implies its statistical characteristics, which is effective for image encoding and decoding. Consequently, content-adaptive compression methods based on learning can better capture the content attributes of images, thereby enhancing encoding performance. However, learned image compression methods do not comprehensively account for both the global and local correlations among the pixels within an image. Moreover, they are constrained by rate-distortion optimization, which prevents the attainment of a compact representation of image attributes. To address these issues, we propose a syntax-guided content-adaptive transform framework that efficiently captures image attributes and enhances encoding efficiency. Firstly, we propose a syntax-refined side information module that fully leverages syntax and side information to guide the adaptive transformation of image attributes. Moreover, to more thoroughly exploit the global and local correlations in image space, we designed global–local modules, local–global modules, and upsampling/downsampling modules in codecs, further eliminating local and global redundancies. The experimental findings indicate that our proposed syntax-guided content-adaptive image compression model successfully adapts to the diverse complexities of different images, which enhances the efficiency of image compression. Concurrently, the method proposed has demonstrated outstanding performance across three benchmark datasets. [ABSTRACT FROM AUTHOR]

The surge in image data has significantly increased the pressure on storage and transmission, posing new challenges for image compression technology. The structural texture of an image implies its statistical characteristics, which is effective for image encoding and decoding. Consequently, content-adaptive compression methods based on learning can better capture the content attributes of images, thereby enhancing encoding performance. However, learned image compression methods do not comprehensively account for both the global and local correlations among the pixels within an image. Moreover, they are constrained by rate-distortion optimization, which prevents the attainment of a compact representation of image attributes. To address these issues, we propose a syntax-guided content-adaptive transform framework that efficiently captures image attributes and enhances encoding efficiency. Firstly, we propose a syntax-refined side information module that fully leverages syntax and side information to guide the adaptive transformation of image attributes. Moreover, to more thoroughly exploit the global and local correlations in image space, we designed global–local modules, local–global modules, and upsampling/downsampling modules in codecs, further eliminating local and global redundancies. The experimental findings indicate that our proposed syntax-guided content-adaptive image compression model successfully adapts to the diverse complexities of different images, which enhances the efficiency of image compression. Concurrently, the method proposed has demonstrated outstanding performance across three benchmark datasets.

The polarizability operator plays a central role in density functional perturbation theory and other perturbative treatment of first principle electronic structure theories. The cost of computing the polarizability operator generally scales as O(Ne4) where Ne is the number of electrons in the system. The recently developed adaptively compressed polarizability operator (ACP) formulation [L. Lin, Z. Xu and L. Ying, Multiscale Model. Simul. 2017] reduces such complexity to O(Ne3) in the context of phonon calculations with a large basis set for the first time, and demonstrates its effectiveness for model problems. In this paper, we improve the performance of the ACP formulation by splitting the polarizability into a near singular component that is statically compressed, and a smooth component that is adaptively compressed. The new split representation maintains the O(Ne3) complexity, and accelerates nearly all components of the ACP formulation, including Chebyshev interpolation of energy levels, iterative solution of Sternheimer equations, and convergence of the Dyson equations. For simulation of real materials, we discuss how to incorporate nonlocal pseudopotentials and finite temperature effects. We demonstrate the effectiveness of our method using one-dimensional model problem in insulating and metallic regimes, as well as its accuracy for real molecules and solids.

A new dynamic Huffman encoding has been proposed recently (Shmuel et al., 2021), which instead of basing itself on the information gathered from the already processed portion of the file, as traditional adaptive codings do, uses rather the information that is still to come. The current work extends this idea to bidirectional adaptive compression, taking both past and future into account, and not only performs at least as good as static Huffman, but also provably improves on the future-only based variant. We extend the technique to arithmetic coding and give both theoretical and empirical results that support the enhancement of the new compression algorithm. [ABSTRACT FROM AUTHOR]

In order to retain effective trajectory information and balance the dynamic correlation between effective trajectory information and global compression efficiency,the vehicle GPS trajectory spatiotemporal adaptive compression based on Douglas Peucker-particle swarm optimization(DP-PSO) algorithm was proposed by massive GPS trajectory data. According to the spatio-temporal characteristics of original trajectory,MDL segmentation framework was used to preliminarily segment the selected effective trajectory at the first stage. The number of segments representing the characteristics of original trajectory was obtained. At the second stage,the vehicle GPS trajectory spatiotemporal adaptive compression based on DP -PSO algorithm was established by influence factors,such as offset angle,perpendicular euclidean distance,trajectory segmentation and so on. All trajectories clustered into a class of trajectory with similar characteristics by dynamic adjusting for threshold parameters. An evaluation index that can fully reflect the spatial and temporal characteristics of the compression results was built. The global optimization and effective compression of all trajectories were realized. Finally,a vehicle GPS trajectory data selected can verify the reliability of the proposed algorithm. The results show that in the comparison of the classical TRACLUS algorithm, the new TCDP, RSF and Top-Down Time Ratio algorithm, the DPPSO not only retains more characteristic trajectory points,but also covers abundant trajectory information in the accuracy of trajectory compression. In terms of simplification,compared with other four algorithms, the average change variation of trajectory compression with the DP-PSO is reduced by 55. 823%, 45. 802%, 50. 815% and 32. 566% respectively. This improvement brings up advantages in complexity, compression accuracy and stability. The compression time is stable at 101 095 ms, improving the overall operation efficiency of trajectory compression. [ABSTRACT FROM AUTHOR]

Objective: Adaptive compression methods in hearing aids have been developed to maximise audibility while preserving temporal envelope modulations. Increasing the number of channels may improve listening comfort for loud sounds. However, the effects of this on speech recognition in different environmental conditions are unknown. This study evaluated the effects of different channel architectures and adaptive compression properties on speech recognition in noise and reverberation. Design: Sentences were mixed with steady or modulated noise at three signal-to-noise ratios (SNRs). These were processed with and without reverberation and amplified with four proprietary adaptive compression methods or linear amplification. Study sample: 36 listeners with mild to moderately-severe hearing loss. Results: Adaptive compression improved speech recognition over linear amplification to a small extent, with no significant differences among methods using 4 or 24 channels or a combination thereof. These effects remained across the different background noise and reverberation conditions. Conclusions: Increasing the number of channels does not negatively affect speech recognition in noise and reverberation when adaptive compression is used. If future research shows that increasing the number of channels improves listening comfort for loud sounds, these results indicate that adaptive compression methods with as many as 24 channels are viable options for hearing aids. [ABSTRACT FROM AUTHOR]

Background: Wide dynamic range compression (WDRC) has been widely used in hearing aid technology. However, several reports indicate that WDRC may improve audibility at the expense of speech intelligibility. As such, a modified amplification compression scheme, named adaptive compression, was developed. However, the effect of compression strategies on speech perception in pediatric hearing aid users has not been clearly reported. Purpose: The purpose of the present study was to investigate the effect of adaptive compression and fast-acting WDRC processing strategies on sentence recognition in noise with Mandarin, pediatric hearing aid users. Research Design: This study was set up using a double-blind, within-subject, repeated-measures design. Study Sample: Twenty-six children who spoke Mandarin Chinese as their primary language and had bilateral sensorineural hearing loss participated in the study. Data Collection and Analysis: Sentence recognition in noise was evaluated in behind-the-ear technology with both adaptive compression processing and fast-acting WDRC processing and was selected randomly for each child. Percent correct sentence recognition in noise with fast-acting WDRC and adaptive compression was collected from each participant. Correlation analysis was performed to examine the effect of gender, age at assessment, and hearing threshold of the better ear on signal-to-noise ratio, and a paired-samples t test was employed to compare the performance of the adaptive compression strategy and fast-acting WDRC processing. Results: The mean percentage correct of sentence recognition in noise with behind-the-ear technology with fast-acting WDRC and adaptive compression processing were 62.24% and 68.71%, respectively. The paired-samples t test showed that the performance of the adaptive compression strategy was significantly better than the fast-acting WDRC processing (t = 3.190, p = 0.004). Conclusions: Compared with the fast-acting WDRC, adaptive compression provided better sentence recognition in noise for Mandarin pediatric hearing aid users. [ABSTRACT FROM AUTHOR]

Phonon calculations based on first principle electronic structure theory, such as the Kohn{Sham density functional theory, have wide applications in physics, chemistry, and material science. The computational cost of first principle phonon calculations typically scales steeply as O(N4e), where Ne is the number of electrons in the system. In this work, we develop a new method for reducing the computational complexity of computing the full dynamical matrix, and hence the phonon spectrum, to O(N³e). The key concept for achieving this is to compress the polarizability operator adaptively with respect to the perturbation of the potential due to the change of the atomic configuration. Such an adaptively compressed polarizability operator allows accurate computation of the phonon spectrum. The reduction of complexity only weakly depends on the size of the band gap, and our method is applicable to insulators as well as semiconductors with small band gaps. We demonstrate the effectiveness of our method using one-dimensional and two-dimensional model problems. [ABSTRACT FROM AUTHOR]

The parallel relay network is studied, in which a single source node sends a message to a single destination node with the help of $N$ parallel relays. Channel coefficients are assumed to vary over time and channel state information (CSI) is causally available only at the receiver side (CSIR). Opportunistic noisy network coding is proposed for intelligently exploiting CSIR at each relay in a distributed manner by operating the noisy network coding scheme with adaptive compression. More specifically, each relay opportunistically vector-quantizes the collection of received symbols that is received with channel gains larger than a certain threshold. It then forwards the digital compression information to the destination node using independently generated Gaussian codes. For independent and identically distributed (i.i.d.) Rayleigh fading, the proposed scheme is shown to achieve the ergodic capacity in the large number of relays regime. Furthermore, the proposed scheme is extensively compared with several alternative schemes, the decode-forward scheme, the adaptive amplify-forward scheme, and the non-adaptive noisy network coding scheme over geometric models. We show that the new proposed scheme provides significant gain over these schemes in various cases. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of lossless universal source coding for stationary memoryless sources on countably infinite alphabets. This task is generally not achievable without restricting the class of sources over which universality is desired. Building on our prior work, we propose natural families of sources characterized by a common dominating envelope. We particularly emphasize the notion of adaptivity, which is the ability to perform as well as an oracle knowing the envelope, without actually knowing it. This is closely related to the notion of hierarchical universal source coding, but with the important difference that families of envelope classes are not discretely indexed and not necessarily nested. Our contribution is to extend the classes of envelopes over which adaptive universal source coding is possible, namely by including max-stable (heavy-tailed) envelopes which are excellent models in many applications, such as natural language modeling. We derive a minimax lower bound on the redundancy of any code on such envelope classes, including an oracle that knows the envelope. We then propose a constructive code that does not use knowledge of the envelope. The code is computationally efficient and is structured to use an expanding threshold for auto-censoring (ETAC), and we therefore dub it the ETAC-code. We prove that the ETAC-code achieves the lower bound on the minimax redundancy within a factor logarithmic in the sequence length, and can be therefore qualified as a near-adaptive code over families of heavy-tailed envelopes. For finite and light-tailed envelopes, the penalty is even less, and the same code follows closely previous results that explicitly made the light-tailed assumption. Our technical results are founded on methods from regular variation theory and concentration of measure. [ABSTRACT FROM AUTHOR]

Adaptive compression for images transmission in resource-constrained multi-hop wireless network applications is considered. In this strategy, development of an energy efficient image compression scheme is proposed as a means to overcome the computation and/or energy limitation of individual nodes. It has the additional benefit of extending the 'life' of individual node by saving its energy power. Two methods for energy efficient image compression are proposed and investigated with respect to energy consumption and image quality. Simulation results show that the proposed scheme prolongs the system lifetime and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses. [ABSTRACT FROM AUTHOR]

Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using six data sets shows that our proposed algorithm can achieve submeter accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm shows that our algorithm can reduce the error by 10-60 cm for the same compression ratio. [ABSTRACT FROM PUBLISHER]

This paper introduces an adaptive compression algorithm for transfer of data streams across operators in stream processing systems. The algorithm is adaptive in the sense that it can adjust the amount of compression applied based on the bandwidth, CPU, and workload availability. It is discriminative in the sense that it can judiciously apply partial compression by selecting a subset of attributes that can provide good reduction in the used bandwidth at a low cost. The algorithm relies on the significant differences that exist among stream attributes with respect to their relative sizes, compression ratios, compression costs, and their amenability to application of custom compressors. As part of this study, we present a modeling of uniform and discriminative mixing, and provide various greedy algorithms and associated metrics to locate an effective setting when model parameters are available at run-time. Furthermore, we provide online and adaptive algorithms for real-world systems in which system parameters that can be measured at run-time are limited. We present a detailed experimental study that illustrates the superiority of discriminative mixing over uniform mixing. [ABSTRACT FROM PUBLISHER]

This paper sheds light on adaptive coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate (log-concave envelope distributions). We prove that the auto-censuring (AC) code is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy and on a careful analysis of the performance of the AC-coding algorithm. The latter relies on nonasymptotic bounds for maxima of samples from discrete distributions with finite and nondecreasing hazard rate. [ABSTRACT FROM AUTHOR]

This paper sheds light on universal coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate. We prove that the auto-censuring (AC) code introduced by Bontemps (2011) is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy by Haussler and Opper (1997) and on a careful analysis of the performance of the AC-coding algorithm. The latter relies on non-asymptotic bounds for maxima of samples from discrete distributions with finite and non-decreasing hazard rate.

The compression of binary bitmap images based on statistical coding with context simulation is considered. Development trends for models and methods of context simulation for an efficient algorithm of compressing binary graphics are discussed. An efficient algorithm of compressing binary graphics is presented. [ABSTRACT FROM AUTHOR]

Infrastructure as a Service clouds often use virtual machines to host different customers on the same physical hardware. This form of resource sharing can lead to unpredicatable performance degradations for the individual customer, especially with regard to data-intensive applications, which heavily depend on stable I/O characteristics. One traditional approach to cope with I/O fluctuations is adaptive online compression. In this paper we present a new scheme for adaptive online compression which has been explicitly designed to work in co-located virtual machine environments. In contrast to existing adaptive online compression schemes, the decision model of our approach does not rely on the system metrics CPU utilization and I/O bandwidth, which we demonstrate to be often displayed inaccurately inside XEN, KVM, and Amazon EC2-based virtual machines. Instead, it only considers the application data rate. Without requiring any calibration or training phase our adaptive compression scheme can improve the I/O throughput of virtual machines up to a factor of four as shown through extended experimental evaluations. [ABSTRACT FROM AUTHOR]

High-quality texture minification techniques, including trilinear and anisotropic filtering, require texture data to be arranged into a collection of pre-filtered texture maps called mipmaps. In this paper, we present a compression scheme for mipmapped textures which achieves much higher quality than current native schemes by exploiting image coherence across mipmap levels. The basic idea is to use a high-quality native compressed format for the upper levels of the mipmap pyramid (to retain efficient minification filtering) together with a novel compact representation of the detail provided by the highest-resolution mipmap. Key elements of our approach include delta-encoding of the luminance signal, efficient encoding of coherent regions through texel runs following a Hilbert scan, a scheme for run encoding supporting fast random-access, and a predictive approach for encoding indices of variable-length blocks. We show that our scheme clearly outperforms native 6:1 compressed texture formats in terms of image quality while still providing real-time rendering of trilinearly filtered textures. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of universal lossless coding on a countable infinite alphabet. It focuses on some classes of sources defined by an envelope condition on the marginal distribution, namely exponentially decreasing envelope classes with exponent \alpha. The minimax redundancy of exponentially decreasing envelope classes is proved to be equivalent to 1 \over 4 \alpha \log e \log^2 n. Then, an adaptive algorithm is proposed, whose maximum redundancy is equivalent to the minimax redundancy. [ABSTRACT FROM PUBLISHER]

Remote visualization techniques that use client-server environments allow users to access large datasets. One possible solution for remote visualization is the use of compression techniques, in which images are generated and compressed at the servers' side and then the encoded images are transferred over a data network, decompressed and displayed at the clients' side. In this paper we propose an adaptive algorithm based on reinforcement learning for choosing one of the available compression methods in order to maximize the frame rate. Our experiments show that such an algorithm can work in a dynamic and uncertain environment, consisting of a visualization server, a visualization client, and a network for transferring the compressed images between the server and the client. [ABSTRACT FROM AUTHOR]

This paper describes universal lossless coding strategies for compressing sources on countably infinite alphabets. Classes of memoryless sources defined by an envelope condition on the marginal distribution provide benchmarks for coding techniques originating from the theory of universal coding over finite alphabets. We prove general upper bounds on minimax regret and lower bounds on minimax redundancy for such source classes. The general upper bounds emphasize the role of the normalized maximum likelihood (NML) codes with respect to minimax regret in the infinite alphabet context. Lower bounds are derived by tailoring sharp bounds on the redundancy of Krichevsky—Trofimov coders for sources over finite alphabets. Up to logarithmic (resp., constant) factors the bounds are matching for source classes defined by algebraically declining (resp., exponentially vanishing) envelopes. Effective and (almost) adaptive coding techniques are described for the collection of source classes defined by algebraically vanishing envelopes. Those results extend our knowledge concerning universal coding to contexts where the key tools from parametric inference are known to fail. [ABSTRACT FROM AUTHOR]