论文标题
DCT域中的对象检测：亮度是解决方案吗？
Object Detection in the DCT Domain: is Luminance the Solution?
论文作者
论文摘要
图像中的对象检测已达到前所未有的性能。最新的方法依赖于提取显着特征的深度体系结构，并预测包含感兴趣对象的边界框。这些方法基本上在RGB图像上运行。但是，RGB图像通常被采购设备以存储目的和转移效率所压缩。因此，对象探测器需要减压。为了提高效率，本文提议利用图像的压缩表示，以执行可在约束资源条件下可用的对象检测。 具体而言，我们专注于JPEG图像，并对新设计的检测体系结构进行彻底分析，以针对JPEG规范的特殊性。与基于标准的RGB架构相比，这导致了$ \ times 1.7 $的速度，而仅将检测性能降低了5.5％。此外，我们的经验发现表明，只有一部分压缩JPEG信息（即亮度组件）才能匹配完整输入方法的检测准确性。
Object detection in images has reached unprecedented performances. The state-of-the-art methods rely on deep architectures that extract salient features and predict bounding boxes enclosing the objects of interest. These methods essentially run on RGB images. However, the RGB images are often compressed by the acquisition devices for storage purpose and transfer efficiency. Hence, their decompression is required for object detectors. To gain in efficiency, this paper proposes to take advantage of the compressed representation of images to carry out object detection usable in constrained resources conditions. Specifically, we focus on JPEG images and propose a thorough analysis of detection architectures newly designed in regard of the peculiarities of the JPEG norm. This leads to a $\times 1.7$ speed up in comparison with a standard RGB-based architecture, while only reducing the detection performance by 5.5%. Additionally, our empirical findings demonstrate that only part of the compressed JPEG information, namely the luminance component, may be required to match detection accuracy of the full input methods.
