Nguyen Rang M H, Brown Michael S
1School of Computing, National University of Singapore, Singapore, Singapore.
2Lassonde School of Engineering, York University, Toronto, Canada.
Int J Comput Vis. 2018;126(6):637-650. doi: 10.1007/s11263-017-1056-0. Epub 2017 Dec 18.
Most camera images are saved as 8-bit standard RGB (sRGB) compressed JPEGs. Even when JPEG compression is set to its highest quality, the encoded sRGB image has been significantly processed in terms of color and tone manipulation. This makes sRGB-JPEG images undesirable for many computer vision tasks that assume a direct relationship between pixel values and incoming light. For such applications, the RAW image format is preferred, as RAW represents a minimally processed, sensor-specific RGB image that is linear with respect to scene radiance. The drawback with RAW images, however, is that they require large amounts of storage and are not well-supported by many imaging applications. To address this issue, we present a method to encode the necessary data within an sRGB-JPEG image to reconstruct a high-quality RAW image. Our approach requires no calibration of the camera's colorimetric properties and can reconstruct the original RAW to within 0.5% error with a small memory overhead for the additional data (e.g., 128 KB). More importantly, our output is a fully self-contained 100% compliant sRGB-JPEG file that can be used as-is, not affecting any existing image workflow-the RAW image data can be extracted when needed, or ignored otherwise. We detail our approach and show its effectiveness against competing strategies.
大多数相机图像都保存为8位标准RGB(sRGB)压缩JPEG格式。即使将JPEG压缩设置为最高质量,编码后的sRGB图像在颜色和色调处理方面也已经过大量处理。这使得sRGB-JPEG图像对于许多假设像素值与入射光之间存在直接关系的计算机视觉任务来说并不理想。对于此类应用,RAW图像格式更受青睐,因为RAW代表了一种经过最少处理的、特定于传感器的RGB图像,它与场景辐射度呈线性关系。然而,RAW图像的缺点是它们需要大量存储空间,并且许多成像应用对其支持不佳。为了解决这个问题,我们提出了一种方法,用于在sRGB-JPEG图像中编码必要的数据,以重建高质量的RAW图像。我们的方法无需对相机的比色特性进行校准,并且可以将原始RAW重建到误差在0.5%以内,同时为额外数据(例如128 KB)带来较小的内存开销。更重要的是,我们的输出是一个完全独立的、100%符合sRGB标准的JPEG文件,可以直接使用,不会影响任何现有的图像工作流程——RAW图像数据可以在需要时提取,否则可以忽略。我们详细介绍了我们的方法,并展示了它相对于竞争策略的有效性。