VVC Extension Scheme for Object Detection Using Contrast Reduction
Takahiro Shindo Taiju Watanabe Kein Yamada Hiroshi Watanabe
In recent years, video analysis using Artificial Intelligence (AI) has been
widely used, due to the remarkable development of image recognition technology
using deep learning. In 2019, the Moving Picture Experts Group (MPEG) has
started standardization of Video Coding for Machines (VCM) as a video coding
technology for image recognition. In the framework of VCM, both higher image
recognition accuracy and video compression performance are required. In this
paper, we propose an extention scheme of video coding for object detection
using Versatile Video Coding (VVC). Unlike video for human vision, video used
for object detection does not require a large image size or high contrast.
Since downsampling of the image can reduce the amount of information to be
transmitted. Due to the decrease in image contrast, entropy of the image
becomes smaller. Therefore, in our proposed scheme, the original image is
reduced in size and contrast, then coded with VVC encoder to achieve high
compression performance. Then, the output image from the VVC decoder is
restored to its original image size using the bicubic method. Experimental
results show that the proposed video coding scheme achieves better coding
performance than regular VVC in terms of object detection accuracy.