N231-038 TITLE: Perceptually Lossless Unmanned Underwater Vehicle (UUV) Sensor Data Compression
OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Artificial Intelligence (AI)/Machine Learning (ML); General Warfighting Requirements (GWR)
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
OBJECTIVE: Develop an innovative data compression capability for the UUV sensor data that can increase onboard storage and enable sending large amounts of sensor data acoustically through the water column and Over the Horizon (OTH) using limited bandwidth transmissions including acoustic, radio, and satellite links.
DESCRIPTION: The Maritime Expeditionary Mine Countermeasures Unmanned Undersea Vehicle (MEMUUV) Family of Systems (FoS) program has an interest in increasing the capability of sending high-resolution sonar and camera images and video files OTH using limited bandwidth. Additionally, the Mine Warfare community uses large volumes of imagery data to build Automatic Target Recognition (ATR) data sets for training and calibration of ATR systems. Today�s through water transfer rates are on the order of 80 bps; however, in the near future it is anticipated that the program will leverage transfer rates of up to 4 kbps at distances between 1500m-4500m. The transmission of compressed data will need to overcome physical challenges such as low signal-to-noise ratios, strong rapidly varying multi-path, and noise interference that with today�s technology results in relatively high error rates. Unique research and development will be required to achieve the required data compression for sonar images due to the speckle noise content. It should be anticipated that file sizes up to 40 MB should be compressed with a visually lossless ratio of at least 10:1.
The Navy seeks tangible improvement over today�s image and data compression rates. State of the art lossless compression methods can currently be expected to achieve 2:1 to 4:1 compression, while perceptually lossless compression methods may achieve 10:1 compression or better [Ref 1].
Recognizing that off-the-shelf (OTS) codecs may not be optimal for sonar and undersea optical modalities, an innovative compression technique is sought for compression of sonar, camera, and video data so that high resolution images and videos can be transmitted over the limited bandwidth, error-prone links.
Concepts are desired that are both bandwidth efficient and error tolerant [Refs 1, 3, 5]. For purposes of this SBIR topic, "visually lossless" means the compressed imagery retains the feature details necessary for mine identification by, or training of, human analysts. It is noted that the Human Visual System with masking (HVSm) correlates well with human perception and is the preferred metric in recognizing perceptual losslessness [Refs 1 and 2]. Offers should notionally quantify expected improvement of the proposed technology. Image compression solutions may be in the form of hardware, software, or both. Hardware proposals should address integration considerations (e.g., size, weight and power [SWaP] constraints) for small and medium class UUVs such as the MK 18 Mod 1 and Mod 2.
Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. Owned and Operated with no Foreign Influence as defined by DOD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence Security Agency (DCSA), formerly the Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DCSA and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advance phases of this contract.
All DoD Information Systems (IS) and Platform Information Technology (PIT) systems will be categorized in accordance with Committee on National Security Systems Instruction (CNSSI) 1253, implemented using a corresponding set of security controls from National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53, and evaluated using assessment procedures from NIST SP 800-53A and DoD-specific (KS) (Information Assurance Technical Authority (IATA) Standards and Tools).
The Contractor shall support the Assessment and Authorization (A&A) of the system. The Contractor shall support the government�s efforts to obtain an Authorization to Operate (ATO) in accordance with DoDI 8500.01 Cybersecurity, DoDI 8510.01 Risk Management Framework (RMF) for DoD Information Technology (IT), NIST SP 800-53, NAVSEA 9400.2-M (October 2016), and business rules set by the NAVSEA Echelon II and the Functional Authorizing Official (FAO). The Contractor shall design the tool to their proposed RMF Security Controls necessary to obtain A&A. The Contractor shall provide technical support and design material for RMF assessment and authorization in accordance with NAVSEA Instruction 9400.2-M by delivering OQE and documentation to support assessment and authorization package development.
Contractor Information Systems Security Requirements. The Contractor shall implement the security requirements set forth in the clause entitled DFARS 252.204-7012, "Safeguarding Covered Defense Information and Cyber Incident Reporting," and National Institute of Standards and Technology (NIST) Special Publication 800-171.
PHASE I: The company will develop a concept for compressing the MEMUUV sonar, video and imagery that meet the requirements in the Description. Demonstrate the feasibility of the concept in meeting Navy needs and establish that the concept can be developed into a useful product for the Navy. Feasibility will be established by analytical modeling and feasibility testing. The Phase I Option, if exercised, will include the initial concept design specifications and capabilities description to build a prototype solution in Phase II.
PHASE II: Based on the results of Phase I and the Phase II development plan, develop and deliver an image compression (hardware and/or software) prototype for evaluation. The prototype will be evaluated to determine its capability in meeting the performance goals defined in the Phase II development plan and the Navy requirements for data compression. System performance will be demonstrated through prototype evaluation with MEMUUV sonar and camera data. Evaluation results will be used to refine the prototype into an initial design that will meet Navy requirements. Prepare a Phase III development plan to transition the technology to Navy use by identifying any remaining cyber or security requirements, training packages, and sustainment costs.
It is possible that the work under this effort will be classified under Phase II (see Description section for details).
PHASE III DUAL USE APPLICATIONS: Support the Navy and other Government and commercial entities (e.g., NOAA, NGIA and underwater survey companies) in transitioning the technology to a fielded system within MK 18 Program of Record or other commercial applications. Conduct efforts to perform any remaining integration or fielding requirements to include training, technical manuals, cyber security, sustainment, and other engineering services. Mature the manufacturing process of any image compression and minimized data loss hardware and software from initial Low-Rate Production (LRIP) through Full Rate Production (FRP). The Phase III will provide the contract instrument for the PMO to apply sustainment and product improvement during the product life cycle.
1. Kwan, Chiman, Jude Larkin, Bence Budavari, Bryan Chou, Eric Shang, and Trac D. Tran. 2019. "A Comparison of Compression Codecs for Maritime and Sonar Images in Bandwidth Constrained Applications," Computers, 8, no. 2: 32. https://doi.org/10.3390/computers8020032
2. Ponomarenko, N.; Silvestri, F.; Egiazarian, K.; Carli, M.; Astola, J.; Lukin, V. "On between-coefficient contrast masking of DCT basis functions," In Proceedings of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM-07, Scottsdale, AZ, USA, 25�26 January 2007.
3. Collins, T. & Atkins, P. "Error-tolerant SPIHT image compression," IEEE Proceedings Vision, Image & Signal Processing, Volume 148, Issue 3, Jun 2001
4. Tomasi, B. & Toni, L. & Casari, P.& Preisig, J. & Zorzi, M. "A Study on the SPIHT Image Coding Technique for Underwater Acoustic Communications," WUWNet '11: Proceedings of the Sixth ACM International Workshop on Underwater Networks, December 2011, Article No.: 9, Pages 1�8t. https://doi.org/10.1145/2076569.2076578
5. Higdon, Thomas. "The Compression of Synthetic Aperture Sonar Images," May 2008, Free books. http://free.ebooks6.com/The-Compression-of-Synthetic-Aperture-Sonar-Images-pdf-e31801.pdf
KEYWORDS: Data compression; image compression; sonar image compression; lossless image compression; visually lossless image compression; sonar image compression algorithms.
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|The topic mentions both compression, and transmission through a lossy channel. Now in traditional Shannon theory, source and channel coding are thought to be independent, but recent work suggests some benefit in joint development. My questions are:
1 As a multiphase effort, how much weight do you put on compression, vs. channel coding. (e.g., in phase 1, 2, etc.)
2. Do you favor a joint source-channel approach specifically, or is separate source and channel coding suitable.
3. You mentioned a specific GPU. Should be assume that is the platform that will be used for source/channel coding and transmission. Alternatively, are you interested in future chipsets if they become available, for efficient processing.
|1. Generating high resolution imagery is necessary for ATR solutions regardless, so the emphasis is on compressing the imagery already produced by the sensors to facilitate transmission. However, all approaches (for Phase I and beyond) will be considered if the final product, containing targets of interest, still displays strong identifying features.
2. No preference here.
3. Yes, the current generation Unmanned Underwater Vehicles (UUVs) utilize the NVidia AGX Xavier. Future chipsets are of interest down the road for next generation vehicles. In all cases, the receiving platform will either be a ruggedized Getac laptop or mobile Dell workstation.
|1. In what formats are sensor data generated? Would the Navy provide representative examples?
2. If a software-only solution is proposed, what hardware can be assumed to be available? (CPU architecture, speed, RAM)
3. �Visually lossless� is defined for this purpose as not affecting mine identification. Would that permit solutions with obvious visual artifacts, so long as they don�t affect identification?
4. Is there a constraint on time latency from image production to transmission, or could images be batched to exploit commonalities for better compression?
5. Would any form of two-way communication be viable, such that the receiving side could give feedback that influences the compression process?
6. A few of the provided references concern the discrete cosine transform (DCT). Is that a baseline approach to compare against, a suggested avenue for investigation, or something else?
|1. Yes, representative data will be provided in the form of JSF, HF, TIFF, common video file formats, etc�
2. The platform of choice on the embedded side is the NVidia AGX Xavier. The receiving side will either be a ruggedized Getac laptop or a mobile Dell workstation. With respect to the Xavier, it is a heavily constrained platform and is already being tasked with running autonomy and Automatic Target Recognition (ATR) solutions.
3. Yes, that is an acceptable solution as long as the targets of interest are still able to be identified.
4. The MK18 Unmanned Underwater Vehicles (UUVs) have several different types of acoustic modem messages. Some of these are necessary for vehicle health as well as other processes and are sent out on a pre-set cycle. The introduction of a new acoustic message type to transmit imagery will need to be integrated into this cycle. In summary, it is likely that a batching approach will align well with the system as opposed to low latency image production to transmission. In parallel with the image compression technologies referenced in this topic, it is likely that new autonomy behaviors will need to be developed in order to realize the overall CONOP.
5. Exploring this avenue is of interest in the future, but the current system does not allow for this CONOP.
6. Merely a suggested avenue for investigation.