ATHENA Christian Doppler (CD) Laboratory

MVCD: Multi-Dimensional Video Compression Dataset (Best paper candidate)

IEEE Visual Communications and Image Processing (IEEE VCIP 2024)

Tokyo, Japan, December 8-11, 2024

[PDF]

Hadi Amirpour (AAU, Austria), Mohammad Ghasempour (AAU, Austria), Farzad Tashtarian (AAU, Austria), Ahmed Telili (TII, UAE), Samira Afzal (AAU, Austria), Wassim Hamidouche (INSA, France), Christian Timmerer (AAU, Austria)

Abstract: In the field of video streaming, the optimization of video encoding and decoding processes is crucial for delivering high-quality video content. Given the growing concern about carbon dioxide emissions, it is equally necessary to consider the energy consumption associated with video streaming. Therefore, to take advantage of machine learning techniques for optimizing video delivery, a dataset encompassing the energy consumption of the encoding and decoding process is needed. This paper introduces a comprehensive dataset featuring diverse video content, encoded and decoded using various codecs and spanning different devices. The dataset includes 1000 videos encoded with four resolutions (2160p, 1080p, 720p, and 540p) at two frame rates (30 fps and 60 fps), resulting in eight unique encodings for each video. Each video is further encoded with four different codecs — AVC (libx264), HEVC (libx265), AV1 (libsvtav1), and VVC (VVenC) — at four quality levels defined by QPs of 22, 27, 32, and 37. In addition, for AV1, three additional QPs of 35, 46, and 55 are considered. We measure both encoding and decoding time and energy consumption on various devices to provide a comprehensive evaluation, employing various metrics and tools. Additionally, we assess encoding bitrate and quality using quality metrics such as PSNR, SSIM, MS-SSIM, and VMAF. All data and the reproduction commands and scripts have been made publicly available as part of the dataset, which can be used for various applications such as rate and quality control, resource allocation, and energy-efficient streaming.

Dataset URL: https://github.com/cd-athena/MVCD

Index Terms— Video encoding, decoding, energy, complexity, quality.

Energy-Quality-aware Variable Framerate Pareto-Front for Adaptive Video Streaming

IEEE Visual Communications and Image Processing (IEEE VCIP 2024)

Tokyo, Japan, December 8-11, 2024

[PDF] [Slides]

Prajit T Rajendran (Universite Paris-Saclay), Samira Afzal (Alpen-Adria-Universität Klagenfurt), Vignesh V Menon (Fraunhofer HHI), Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: Optimizing framerate for a given bitrate-spatial resolution pair in adaptive video streaming is essential to maintain perceptual quality while considering decoding complexity. Low framerates at low bitrates reduce compression artifacts and decrease decoding energy. We propose a novel method, Decoding-complexity aware Framerate Prediction (DECODRA), which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints. DECODRA dynamically adjusts the framerate based on current bitrate and spatial resolution, balancing trade-offs between framerate, perceptual quality, and decoding complexity. Extensive experimentation with the Inter-4K dataset demonstrates DECODRA’s effectiveness, yielding an average PSNR and VMAF increase of 0.87 dB and 5.14 points, respectively, for the same bitrate compared to the default 60 fps encoding. Additionally, DECODRA achieves an average reduction in decoding energy consumption of 13.27 %, enhancing the viewing experience, extending mobile device battery life, and reducing the energy footprint of streaming services.

Index Terms—Adaptive video streaming; Decoding complexity; Framerate optimization; Perceptual quality; Decoding energy consumption

Between July and August 2024, the ATHENA Christian Doppler Laboratory hosted four interns working on the following topics:

• Halime Lezi: Image and Video Compression Pipeline
• Luka Kaiser: VidStream
• Julius van Dillen: Enhancing Video Quality with Super-Resolution

At the conclusion of their internships, they presented their work and results, receiving official certificates from the university. This collaboration was mutually beneficial for both the researchers at ATHENA and the interns. Their learning process was enhanced by the dedicated guidance they received, which included personalized mentorship, hands-on training, and continuous support. This comprehensive supervision ensured that they not only developed practical skills but also gained a deeper understanding of the research methodologies and technologies used in the video streaming field. We extend our gratitude to all three interns for their genuine interest, productive efforts, and valuable feedback on the laboratory.

Halime Lezi: “I had an awesome time during my four-week internship at ATHENA. My project was about image and video compression, and I learned a lot about how it works. I also got to use Python, which was both fun and challenging. The work environment at ATHENA was really supportive and interesting. My supervisor, Emanuele Artioli, was super helpful and always ready to answer my questions. He made sure I understood both the practical and theoretical parts of my work, which was really cool. It was also great to work with people from different countries. The team was friendly, and we got along well. The work-life balance was good, with a nice mix of work and relaxation. Overall, my time at ATHENA was very educational and enjoyable. The skills and knowledge I gained during this internship will be really useful for my future studies and career. I’m thankful for the opportunity and the support I received. I would highly recommend this internship to anyone looking for a rewarding and fulfilling experience. It’s a great place to learn, grow, and meet new people.”

Luka Kaiser: “I had an amazing four weeks at ATHENA. It was really nice meeting new colleagues and even making new friends. The project I worked on was exactly what interests me, and if I had any questions, my supervisor, Christian, was always there to help me out. So, thank you very much for that. Overall, this experience has been incredibly valuable, and I learned a lot and gained practical skills that I will definitely use in the future. My time here was both productive and enjoyable. I am grateful for the opportunity and would love to stay connected with everyone I’ve met. Thank you once again for everything.”

Julius van Dillen: “My four-week internship at ATHENA was incredible. I focused on improving image and video quality through Super-Resolution techniques. I had the opportunity to work with a variety of tools and technologies, including FFmpeg, Visual Studio Code, Python, several Super-Resolution architectures, and various video quality metrics. I was truly surprised by how much Super-Resolution enhances video and image quality. I really enjoyed working with these technologies and found the entire process fascinating. I am grateful to have had Daniele as my supervisor; his guidance and support made the experience both easier and more enjoyable. During my internship, I gained valuable insights into the research process and the fundamentals of Python programming.”

Energy-Efficient Spatial and Temporal Resolution Selection for Per-Title Encoding

IEEE Access

[PDF]

Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)

Abstract: Video streaming has become an integral part of our digital lives, driving the need for efficient video delivery. With the growing demand for seamless video delivery, adaptive video streaming has emerged as a solution to support users with varying device capabilities and network conditions. Traditional adaptive streaming relies on a predetermined set of bitrate-resolution pairs, known as bitrate ladders, for encoding. However, this “one-size-fits-all” approach is suboptimal when dealing with diverse video content. Consequently, per-title encoding approaches dynamically select the bitrate ladder for each content. However, in an era when carbon dioxide emissions have become a paramount concern, it is crucial to consider energy consumption. Therefore, this paper addresses the pressing issue of increasing energy consumption in video streaming by introducing a novel approach, ESTR, which goes beyond traditional quality-centric resolution selection approaches. Instead, the ESTR considers both video quality and decoding energy consumption to construct an optimal bitrate ladder tailored to the unique characteristics of each video content. To accomplish this, ESTR encodes each video content using a range of spatial and temporal resolutions, each paired with specific bitrates. It then establishes a maximum acceptable quality drop threshold (τ), carefully selecting resolutions that not only preserve video quality above this threshold but also minimize decoding energy consumption. Our experimental results, at a fixed τ of 2 VMAF steps, demonstrate a 32.87% to 41.86% reduction in decoding energy demand for HEVC-encoded videos across various software decoder implementations and operating systems, with a maximum bitrate increase of 2.52%. Furthermore, on a hardware-accelerated client device, a 46.37% energy saving was achieved during video playback at the expense of a 2.52% bitrate increase. Remarkably, these gains in energy efficiency are achieved while maintaining consistent video quality.