The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic. The demand for higher resolutions and quality, the need for immersive video formats, and the newest, more complex video codecs increase the energy consumption in data centers and display devices. In this paper, we explore and compare the energy consumption across optimized state-of-the-art video codecs, SVT-AV1, VVenC/VVdeC, VP9, and x.265. Furthermore, we align the energy usage with various objective quality metrics and the compression performance for a set of video sequences across different resolutions. The results indicate that from the tested codecs and configurations, SVTAV1 provides the best tradeoff between energy consumption and quality. The reported results aim to serve as a guide towards sustainable video streaming while not compromising the quality of experience of the end user.

Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions. Motivated by the evidence in biological science, our work develops a novel approach for similarity search. Fundamentally different from existing methods that typically reduce the dimension of the data to lessen the computational complexity and speed up the search, our approach projects the data into an even higher-dimensional space while ensuring the sparsity of the data in the output space, with the objective of further improving precision and speed. Specifically, our approach has two key steps. Firstly, it computes the optimal sparse lifting for given input samples and increases the dimension of the data while approximately preserving their pairwise similarity. Secondly, it seeks the optimal lifting operator that best maps input samples to the optimal sparse lifting. Computationally, both steps are modeled as optimization problems that can be efficiently and effectively solved by the Frank-Wolfe algorithm. Simple as it is, our approach has reported significantly improved results in empirical evaluations, and exhibited its high potentials in solving practical problems.