SBIR Phase I: Improved image compression targeting machine learning based detection algorithms

SBIR 第一阶段：针对基于机器学习的检测算法改进图像压缩

• 批准号: 2233091
• 负责人: Ravikiran Gopalan
• 金额: $ 27.46万
• 依托单位: AI ACUITY
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233091&HistoricalAwards=false
• 关键词: SBIR; Phase; Improved; image; compression

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in reducing the sizes of earth observation images, thereby saving millions of dollars on transmission, storage and processing costs. Storage and processing costs account for a large portion of costs for earth observation companies, reducing these costs will make earth observation imagery more accessible for a broader audience including companies performing climate and environment studies. The need for reduced image sizes also extends to video compression. Video calls, telepresence, telemedicine, remote work and metaverse all depend on the ability to stream video over limited or variable bandwidth connections. This technology will find ready applications in the area of high-efficiency video compression.This SBIR Phase I project uses object detection algorithms to detect areas of importance in an image and utilizes that information to improve the efficiency of image compression. More specifically, the research will develop a compression network that maximizes the detection accuracy of a down-stream, machine learning-based object detector. In contrast, current compression algorithms do not interpret the images they are compressing and simply minimize a visual loss function that treats the entire image equally. The technology will produce images that can be stored in the standard image compression file formats including .png and .jpeg. This technology will enable fast compression and will explore modified compression architectures, quantization, pruning and parallelization using graphics processing units to reduce latency of compression.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力在于缩小地球观测图像的大小，从而节省数百万美元的传输、存储和处理成本。存储和处理成本占地球观测公司成本的很大一部分，降低这些成本将使包括进行气候和环境研究的公司在内的更广泛受众更容易获得地球观测图像。减小图像尺寸的需求也延伸到视频压缩。视频通话、远程呈现、远程医疗、远程工作和元宇宙都依赖于通过有限或可变带宽连接传输视频的能力。该技术将在高效视频压缩领域找到现成的应用。SBIR 第一阶段项目使用对象检测算法来检测图像中的重要区域，并利用该信息来提高图像压缩的效率。更具体地说，该研究将开发一种压缩网络，最大限度地提高下游基于机器学习的物体检测器的检测精度。相比之下，当前的压缩算法不会解释它们正在压缩的图像，而只是最小化平等对待整个图像的视觉损失函数。该技术将生成可以以标准图像压缩文件格式（包括 .png 和 .jpeg）存储的图像。该技术将实现快速压缩，并将使用图形处理单元探索改进的压缩架构、量化、修剪和并行化，以减少压缩延迟。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。