[C57] Adaptive Image Downscaling for Rate-Accuracy-Latency Optimization of Task-Target Image Compression


As the computational cost of the compression frame- work increases due to improved image codecs and the prolif- eration of high-resolution images, downscaling can be used to reduce compression overhead before compression process. There are several existing deep learning-based downscaling approaches, but these studies have the limitation of outputting fixed sizes for all different input images. In this paper, we propose a novel approach of adaptive image downscaling framework for rate- accuracy-latency optimization. We utilize deep learning-based downscaling network that learns which size factor to use in downscaling operation adjusting the trade-off between rate and accuracy through λ. Our experimental results show that the proposed framework enhances the rate-accuracy performance of compression rate control or uniform downscaling by up to 43.5% BD-rate (mAP), while remaining minimal latency at the same accuracy compared to others.

The 6th IEEE International Conference on Artificial Intelligence Circuits and Systems
Seongmoon Jeong (정성문)
Seongmoon Jeong (정성문)
Combined MS-PhD student