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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.

Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. One approach to compress point clouds is the video based point cloud compression (V-PCC) technique which projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using the coding tools offered by modern video coding standard like HEVC.

Flow-based generative models are successfully applied in image generation tasks, where an invertible neural network (INN) is built up based on flow steps. Learning-based compression commonly transforms the input into a compact space and then implements a reconstruction network in the decoder accordingly. By utilizing low-resolution images, traditional or adaptive downsamplers with their corresponding traditional or learned upsamplers usually achieve better coding quality at a low bit-rate.

Existing compression artifacts reduction methods aim to restore images on pixel-level, which can improves human visual experience. However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks usually by Deep Neural Networks (DNN). One fundamental problem here is whether existing artifacts reduction methods can help DNNs improve the performance of the high-level tasks. In this paper, we find that these methods have limited performance improvements to high-level tasks, even bring negative effects.