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Saliency-driven image and video coding for humans has gained importance in the recent past. In this paper, we propose such a saliency-driven coding framework for the video coding for machines task using the latest video coding standard Versatile Video Coding (VVC). To determine the salient regions before encoding, we employ the real-time-capable object detection network You Only Look Once (YOLO) in combination with a novel decision criterion. To measure the coding quality for a machine, the state-of-the-art object segmentation network Mask R-CNN was applied to the decoded frame.

Saliency-driven image and video coding for humans has gained importance in the recent past. In this paper, we propose such a saliency-driven coding framework for the video coding for machines task using the latest video coding standard Versatile Video Coding (VVC). To determine the salient regions before encoding, we employ the real-time-capable object detection network You Only Look Once (YOLO) in combination with a novel decision criterion. To measure the coding quality for a machine, the state-of-the-art object segmentation network Mask R-CNN was applied to the decoded frame.

Slides and poster presented during ICASSP 2021 about our work on "Relying on a rate constraint to reduce Motion Estimation complexity".

This paper presents a software-based method for estimating the power consumption of video decoders on various Android devices. Using this method, we developed an automatic system that consists of the VEQE Android application to measure the power consumption of video decoders and a server to collect the metrics. The system allowed us to create power-consumption and decoding-speed dataset for video decoders operating on 236 devices, representing 147 models.

This paper presents a software-based method for estimating the power consumption of video decoders on various Android devices. Using this method, we developed an automatic system that consists of the VEQE Android application to measure the power consumption of video decoders and a server to collect the metrics. The system allowed us to create power-consumption and decoding-speed dataset for video decoders operating on 236 devices, representing 147 models.