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1. Energy Consumption and Carbon Emissions of Modern Software Video Encoders

Author

Chachou, Taieb, Hamidouche, Wassim, Fezza, Sid Ahmed, and Belalem, Ghalem

Abstract

In today's digital landscape, video streaming holds an important role in internet traffic, driven by the pervasive use of mobile devices and the surge in streaming platform popularity. In light of this, the imperative to gauge energy consumption takes center stage, paving the way for ecoconscious and sustainable video streaming solutions with a minimal carbon dioxide (CO2) footprint. This article meticulously examines the energy consumption and CO2 emissions of five popular open-source and fast video encoders: x264, x265, VVenC, libvpx-vp9, and SVT-AV1. These encoders are optimized software implementations of three video coding standards (AVC/H.264, HEVC/H.265, VVC/H.266) and two video formats (VP9 and AV1). To ensure a fair comparison, we also assess coding efficiency across these encoders at four distinct presets, applying three objective quality metrics. Additional factors like computing density and memory usage are considered. Our experiments employ the JVET-CTC video dataset, encompassing video sequences of diverse content and resolutions. Encoding is executed on an Intel x86 multicore processor, while CO2 emissions are computed based on the energy mix data from a server situated in France, reflecting an average emission rate of 101 g/kWh. Our findings underscore that the x264 and SVT-AV1 encoders, especially at fast and faster presets, exhibit the lowest energy consumption and CO2 emissions. Notably, x264 boasts the most energy-efficient performance, yielding CO2 emissions of 0.28, 0.91, 2.07, and 9.74 g when encoding videos using faster, fast, medium, and slower presets, respectively. Furthermore, SVT-AV1 and VVenC encoders operating at a slower preset demonstrate superior coding efficiency, albeit at the cost of higher computational complexity and CO2 emissions of 60.5 g and 406 g, respectively. A salient observation from our study is that resolution and encoder presets serve as crucial parameters for curbing energy consumption and CO2 emissions, albeit with an inherent tradeoff in video quality. Comprehensive results from this research are publicly accessible.a a [Online]. Available: https://chachoutaieb.github.io/encoding_energy_co2

Published
2024
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3. 2BiVQA: Double Bi-LSTM-based Video Quality Assessment of UGC Videos.

Author

Telili, Ahmed, Fezza, Sid Ahmed, Hamidouche, Wassim, and Brachemi Meftah, Hanene F. Z.

Subjects
CONVOLUTIONAL neural networks, RECURRENT neural networks, VIDEO monitors, USER-generated content, VIDEO coding, INTERNET traffic, VIDEOS, COMPUTATIONAL neuroscience
Abstract

Recently, with the growing popularity of mobile devices as well as video sharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet. Unlike professionally generated videos produced by filmmakers and videographers, typically, UGC videos contain multiple authentic distortions, generally introduced during capture and processing by naive users. Quality prediction of UGC videos is of paramount importance to optimize and monitor their processing in hosting platforms, such as their coding, transcoding, and streaming. However, blind quality prediction of UGC is quite challenging, because the degradations of UGC videos are unknown and very diverse, in addition to the unavailability of pristine reference. Therefore, in this article, we propose an accurate and efficient Blind Video Quality Assessment (BVQA) model for UGC videos, which we name 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consists of three main blocks, including a pre-trained Convolutional Neural Network to extract discriminative features from image patches, which are then fed into two Recurrent Neural Networks for spatial and temporal pooling. Specifically, we use two Bi-directional Long Short-term Memory networks, the first is used to capture short-range dependencies between image patches, while the second allows capturing long-range dependencies between frames to account for the temporal memory effect. Experimental results on recent large-scale UGC VQA datasets show that 2BiVQA achieves high performance at lower computational cost than most state-of-the-art VQA models. The source code of our 2BiVQA metric is made publicly available at https://github.com/atelili/2BiVQA. [ABSTRACT FROM AUTHOR]

Published
2024
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