Video Coding for Machines: Large-Scale Evaluation of Deep Neural Networks Robustness to Compression Artifacts for Semantic Segmentation

Alban Marie, Karol Desnos, Luce Morin, Lu Zhang

Abstract: In the Video Coding for Machines (VCM) context where visual content is compressed before being transmitted to a vision task algorithm, appropriate trade-off between the compression level and the vision task performance must be chosen. In this paper, a Deep Neural Networks (DNN) based semantic segmentation algorithm robustness to compression artifacts is evaluated with a total of 1486 different coding configurations. Results indicate the importance of using an appropriate image resolution to overcome the block-partitioning limitations in existing compression algorithms, allowing 58.3%, 49.8%, 33.5% and 24.3% bitrate savings at equivalent prediction accuracy for JPEG, JM, x265 and VVenC, respectively. Surprisingly, JPEG can achieve 73.41% bitrate reduction with the inclusion of compressed images at training time over VVC Test Model (VTM) with a DNN trained on pristine data, which implies that DNN generalization ability must not be overlooked.