Real-time rate distortion-optimized image compression with region of interest on the ARM architecture for underwater robotics applications

Rubino, Eduardo M.; Álvares, Alberto J.; Marı́n, R.; Sanz, Pedro J.

doi:10.1007/s11554-018-0833-5

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…Surv. 0:14 • Jakub Žádník, Markku Mäkitalo, Jarno Vanne, and Pekka Jääskeläinen Some slower results come from implementations on small-scale embedded devices with a limited computational capacity [6,121].…”

Section: Discussionmentioning

confidence: 99%

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Image and Video Coding Techniques for Ultra-low Latency

et al. 2022

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The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.

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Section: Discussionmentioning

confidence: 99%

“…Rubino et al [120,121] proposed a novel image compression algorithm depth embedded block tree (DEBT) in the context of underwater robotics applications. DEBT is a DWT-based progressive compression scheme that does not have an explicit entropy coding step.…”

Section: Othermentioning

confidence: 99%

Image and Video Coding Techniques for Ultra-low Latency

et al. 2022

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“…The transmission of the images to the operator without the use of umbilical is possible thanks to an advanced compression algorithm with region-of-interest selection, which allows fo adaptation of the image size to the currently available bandwidth via radio frequency and sonar channels, obtaining appropriated image quality with image sizes of several hundreds of bytes [5]. Current efforts are trying to extend the system to Visual Light Communications (VLC) modems.…”

Section: Twinbot Projectmentioning

confidence: 99%

Cooperative and Multimodal Capabilities Enhancement in the CERNTAURO Human–Robot Interface for Hazardous and Underwater Scenarios

et al. 2020

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The use of remote robotic systems for inspection and maintenance in hazardous environments is a priority for all tasks potentially dangerous for humans. However, currently available robotic systems lack that level of usability which would allow inexperienced operators to accomplish complex tasks. Moreover, the task’s complexity increases drastically when a single operator is required to control multiple remote agents (for example, when picking up and transporting big objects). In this paper, a system allowing an operator to prepare and configure cooperative behaviours for multiple remote agents is presented. The system is part of a human–robot interface that was designed at CERN, the European Center for Nuclear Research, to perform remote interventions in its particle accelerator complex, as part of the CERNTAURO project. In this paper, the modalities of interaction with the remote robots are presented in detail. The multimodal user interface enables the user to activate assisted cooperative behaviours according to a mission plan. The multi-robot interface has been validated at CERN in its Large Hadron Collider (LHC) mockup using a team of two mobile robotic platforms, each one equipped with a robotic manipulator. Moreover, great similarities were identified between the CERNTAURO and the TWINBOT projects, which aim to create usable robotic systems for underwater manipulations. Therefore, the cooperative behaviours were validated within a multi-robot pipe transport scenario in a simulated underwater environment, experimenting more advanced vision techniques. The cooperative teleoperation can be coupled with additional assisted tools such as vision-based tracking and grasping determination of metallic objects, and communication protocols design. The results show that the cooperative behaviours enable a single user to face a robotic intervention with more than one robot in a safer way.

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“…In recent years, lots of methods were proposed to compress underwater images, which can be divided into three pre-processing method to remove the visual redundancy, and adopts a Wavelet Tree-based Wavelet Difference Reduction (WTWDR) algorithm to remove the spatial redundancy of underwater color images. Recently, Rubino et al [6], an image compression algorithm based on a novel minimal time parallel DWT algorithm is presented. Another powerful class of methods is the Compressed Sensing (CS)-based where the sampling and compressing processes are synchronous instead of two independent as in the transform-based methods [7].…”

Section: Related Workmentioning

confidence: 99%

A Data Compression Module for the SUNSET Platform

Avola

Bernardi

Pannone

et al. 2020

Global Oceans 2020: Singapore – U.S. Gulf Coast

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In the last two decades, visual data acquisition in underwater environments has dramatically increased due to the need to face a wide range of challenges that still require further research, including site monitoring, seabed anomaly detection, object detection and classification, and many others. Most of these activities require frequent data acquisition and processing over time, even at different altitudes, view angles, and perspectives. Recent improvements of small-scale Autonomous Underwater Vehicles (AUVs), in terms of navigation time, automatic control, and onboard processing, are making these submersible vehicles particularly suitable for activities as those reported above. Moreover, thanks to their cableless navigation, limited size, and agility, small-scale AUVs (hereinafter simply AUVs) can reach sites otherwise inaccessible with other kinds of underwater vehicles (e.g., medium and large AUVs). The payload capacity of current AUVs allows us to equip them with different vision sensors, including Red Green Blue (RGB) camera and Side Scan Sonar (SSS). In this context, an open issue remains the efficient transmission of visual data from AUV through an underwater acoustic network to allow a remote workstation an online and/or real-time data processing. In this paper, a data compression module for the SUNSET platform is presented. The module is composed of a set of novel algorithms that enables compression of RGB and SSS information with and without data loss. The module also implements some novel features, including progressive compression and Region Of Interest (ROI) selection; the first used to gradually transmit the image data (e.g., sites in which the acoustic transmission is a hard task), the second used to transmit, with higher quality than the rest of the image, the items contained in a specific area. Exhaustive experiments on RGB and SSS datasets prove the effectiveness of the presented module.

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Real-time rate distortion-optimized image compression with region of interest on the ARM architecture for underwater robotics applications

Cited by 10 publications

References 36 publications

Image and Video Coding Techniques for Ultra-low Latency

Image and Video Coding Techniques for Ultra-low Latency

Cooperative and Multimodal Capabilities Enhancement in the CERNTAURO Human–Robot Interface for Hazardous and Underwater Scenarios

A Data Compression Module for the SUNSET Platform

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