Compression techniques for improving underwater acoustic transmission of images and data

Eastwood, Robert L.; Freitag, Lee; Catipovic, Josko

doi:10.1109/oceans.1996.566719

Cited by 19 publications

(12 citation statements)

References 4 publications

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“…These algorithms form the basis for a number of generic compression utilities; gzip, winzip, and pkzip utilities make use of the Deflate algorithm, while bzip2 uses the BWT algorithm. As early as 1996, Eastwood et al looked at the efficacy of these techniques, and proposed a couple of heuristics for use when transmitting data over an acoustic modem [19]. While these algorithms perform well with plain text and other widely used document formats, they do not perform particularly well on floating-point scientific data.…”

Section: Methods For Lossless Compressionmentioning

confidence: 99%

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Murphy¹

2008

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Oceanographic applications of robotics are as varied as the undersea environment itself. As underwater robotics moves toward the study of dynamic processes with multiple vehicles, there is an increasing need to distill large volumes of data from underwater vehicles and deliver it quickly to human operators. While tethered robots are able to communicate data to surface observers instantly, communicating discoveries is more difficult for untethered vehicles. The ocean imposes severe limitations on wireless communications; light is quickly absorbed by seawater, and tradeoffs between frequency, bitrate and environmental effects result in data rates for acoustic modems that are routinely as low as tens of bits per second. These data rates usually limit telemetry to state and health information, to the exclusion of mission-specific science data.In this thesis, I present a system designed for communicating and presenting science telemetry from untethered underwater vehicles to surface observers. The system's goals are threefold: to aid human operators in understanding oceanographic processes, to enable human operators to play a role in adaptively responding to mission-specific data, and to accelerate mission planning from one vehicle dive to the next. The system uses standard lossy compression techniques to lower required data rates to those supported by commercially available acoustic modems (O(10) -O(100) bits per second).As part of the system, a method for compressing time-series science data based upon the Discrete Wavelet Transform (DWT) is explained, a number of low-bitrate image compression techniques are compared, and a novel user interface for reviewing transmitted telemetry is presented. Each component is motivated by science data from a variety of actual Autonomous Underwater Vehicle (AUV) missions performed in the last year.

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Section: Methods For Lossless Compressionmentioning

confidence: 99%

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Murphy¹

2008

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“…evaluated the performance of an early wavelet-based compressor, EPIC, and found that it had benefits at low bitrates relative to JPEG [28]. In addition, there has been some previous study indicating wavelet compression techniques are particularly applicable to underwater images, video, and acoustic imagery [44,46,47].…”

Section: Imagerymentioning

confidence: 99%

Progressively communicating rich telemetry from autonomous underwater vehicles via relays

Murphy¹

2012

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As analysis of imagery and environmental data plays a greater role in mission construction and execution, there is an increasing need for autonomous marine vehicles to transmit this data to the surface. Without access to the data acquired by a vehicle, surface operators cannot fully understand the state of the mission. Communicating imagery and high-resolution sensor readings to surface observers remains a significant challenge -as a result, current telemetry from free-roaming autonomous marine vehicles remains limited to 'heartbeat' status messages, with minimal scientific data available until after recovery. Increasing the challenge, longdistance communication may require relaying data across multiple acoustic hops between vehicles, yet fixed infrastructure is not always appropriate or possible.In this thesis I present an analysis of the unique considerations facing telemetry systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration. These considerations include high-cost vehicle nodes with persistent storage and significant computation capabilities, combined with human surface operators monitoring each node. I then propose mechanisms for interactive, progressive communication of data across multiple acoustic hops. These mechanisms include wavelet-based embedded coding methods, and a novel image compression scheme based on texture classification and synthesis. The specific characteristics of underwater communication channels, including high latency, intermittent communication, the lack of instantaneous end-to-end connectivity, and a broadcast medium, inform these proposals. Human feedback is incorporated by allowing operators to identify segments of data that warrant higher quality refinement, ensuring efficient use of limited throughput. I then analyze the performance of these mechanisms relative to current practices.Finally, I present CAPTURE, a telemetry architecture that builds on this analysis. CAPTURE draws on advances in compression and delay tolerant networking to enable progressive transmission of scientific data, including imagery, across mul-3 tiple acoustic hops. In concert with a physical layer, CAPTURE provides an endto-end networking solution for communicating science data from autonomous marine vehicles. Automatically selected imagery, sonar, and time-series sensor data are progressively transmitted across multiple hops to surface operators. Human operators can request arbitrarily high-quality refinement of any resource, up to an error-free reconstruction. The components of this system are then demonstrated through three field trials in diverse environments on SeaBED, OceanServer and Bluefin AUVs, each in different software architectures.

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“…For example, in hierarchically-structured compression algorithms such as EPIC [4] and SPIHT [7], representational error in the decompressed image tends to be concentrated in areas of variance extrema (e.g., detailed or high-contrast target neighborhoods and bland backgrounds). In the case of AIR operations, such error can obscure local statistical properties employed by constant false-alarm rate (CFAR) filters such as the adaptive double-gated filter (ADGF) [8].…”

Section: Introductionmentioning

confidence: 99%

“…Performance analysis of each measure is couched in terms of computational cost, sensitivity to error, and relevance to ATh scenarios. Examples are given for template-and codebook-based transforms such as JPEG, VPIC, EPIC, and EBLAST [2][3][4][5]. …”

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confidence: 99%

<title>Image quality measures for performance assessment of compresssion transforms</title>

1998

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Image compression is increasingly employed in the exploitation of limited communication channel bandwidth and maximization of storage system efficiency. Although traditional measures of signal or image quality (e.g., mean-squared error or MSE) are useful in a communication context, few such measures are evident in the open literature that effectively address implementational issues such as visual quality or suitability for automated target recognition (ATh) of a decompressed image. For example, reconstruction errors such as blocking effect, aliasing or suppression of high frequencies, preservation of image or neighborhood statistics, and quantization-induced perturbation of line segments or arcs can degrade visual image quality.In numerous ATh applications, such error is an important source of ATh algorithm performance degradation. Unfortunately, the majority of published image quality measures (IQMs) tend to be based on ad hoc or first-order models of human visual system (HVS) function. Such models do not necessarily correlate with the appearance of image details (e.g., higherorder models) or mathematical descriptions of ATR filters (e.g., non-HVS models). Additionally, published IQMs tend not to address the primary ATR problem of feature visibility or the secondary problem of estimating image or neighborhood error distributions in decompressed imagery.In this paper, a collection of performance measures and IQMs designed specifically for image compression is presented. Beginning with the customary performance measures of MSE and space-time bandwidth product, we progress to spatial measures such as cutoff frequency, effect of greyscale quantization on variance, measures of texture preservation, and statistics of the modulation transfer function (MTF). Texture is estimated by an area-based variance descriptor derived from the Lorentzian distribution, which has been successfully employed in ATh practice [1]. Measures for disruption of linear features such as line segments and arcs are based on analysis of the Hough transform and spatial linear regression. Performance analysis of each measure is couched in terms of computational cost, sensitivity to error, and relevance to ATh scenarios. Examples are given for template-and codebook-based transforms such as JPEG, VPIC, EPIC, and EBLAST [2][3][4][5].

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Compression techniques for improving underwater acoustic transmission of images and data

Cited by 19 publications

References 4 publications

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Progressively communicating rich telemetry from autonomous underwater vehicles via relays

<title>Image quality measures for performance assessment of compresssion transforms</title>

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