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

这项小企业创新研究（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尺寸，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，降低这些成本将使更广泛的受众更容易获得地球观测图像，包括进行气候和环境研究的公司。对缩小图像尺寸的需求也扩展到视频压缩。视频通话、远程呈现、远程医疗、远程工作和虚拟世界都依赖于在有限或可变带宽连接上传输视频的能力。该技术将在高效视频压缩领域找到现成的应用。这个SBIR第一阶段项目使用目标检测算法来检测图像中的重要区域，并利用这些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，以最大限度地提高基于机器学习的下游对象检测器的检测精度。相比之下，当前的压缩算法不能解释它们正在压缩的图像，只是简单地最小化视觉损失函数，该函数对整个图像一视同仁。该技术将生成可以存储在标准图像压缩文件格式（包括。png和。jpeg）中的图像。该技术将实现快速压缩，并将探索改进的压缩架构、量化、修剪和并行化，使用图形处理单元来减少压缩延迟。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.