ATHENA Christian Doppler (CD) Laboratory

IEEE Transactions on Image Processing (TIP)

[PDF]

Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria), Christine Guillemot (INRIA, France), Mohammad Ghanbari (University of Essex, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: Light field imaging, which captures both spatial and angular information, improves user immersion by enabling post-capture actions, such as refocusing and changing view perspective. However, light fields represent very large volumes of data with a lot of redundancy that coding methods try to remove. State-of-the-art coding methods indeed usually focus on improving compression efficiency and overlook other important features in light field compression such as scalability. In this paper, we propose a novel light field image compression method that enables (i) viewport scalability, (ii) quality scalability, (iii) spatial scalability, (iv) random access, and (v) uniform quality distribution among viewports, while keeping compression efficiency high. To this end, light fields in each spatial resolution are divided into sequential viewport layers, and viewports in each layer are encoded using the previously encoded viewports. In each viewport layer, the available viewports are used to synthesize intermediate viewports using a video interpolation deep learning network. The synthesized views are used as virtual reference images to enhance the quality of intermediate views. An image super-resolution method is applied to improve the quality of the lower spatial resolution layer. The super-resolved images are also used as virtual reference images to improve the quality of the higher spatial resolution layer.
The proposed structure also improves the flexibility of light field streaming, provides random access to the viewports, and increases error resiliency. The experimental results demonstrate that the proposed method achieves a high compression efficiency and it can adapt to the display type, transmission channel, network condition, processing power, and user needs.

Keywords—Light field, compression, scalability, random access, deep learning.

The threat of climate change requires a drastic reduction of global greenhouse gas (GHG) emissions in several societal spheres. Thus, this also applies to reducing and rethinking the energy consumption of digital technologies. Video streaming technology is responsible for more than half of digital technology’s global impact [ref]. There is rapid growth, also now with digital and remote work has become more mainstream, in the amount of video data volume, processing of video content, and streaming which affects the rise of energy consumption and its associated GHG emissions.

The International Workshop on Green Multimedia Systems 2023 (GMSys 2023) aims to bring together experts and researchers to present and discuss recent developments and challenges for energy reduction in multimedia systems. This workshop focuses on innovations, concepts, and energy-efficient solutions from video generation to processing, delivery, and further usage.

Find further info at  https://athena.itec.aau.at/events/events-gmsys23/

29th International Conference on MultiMedia Modeling
9 – 12 January, 2023 | Bergen, Norway

Conference Website

[PDF][Slides]

Daniele Lorenzi (AAU, Austria), Farzad Tashtarian (AAU, Austria), Hadi Amirpour (AAU, Austria), Christian Timmerer (AAU, Austria), and Hermann Hellwagner (AAU, Austria)

Abstract:

HTTP Adaptive Streaming (HAS) is the predominant technique to deliver video contents across the Internet with the increasing demand of its applications. With the evolution of videos to deliver more immersive experiences, such as their evolution in resolution and framerate, highly efficient video compression schemes are required to ease the burden on the delivery process. While AVC/H.264 still represents the most adopted codec, we are experiencing an increase in the usage of new generation codecs (HEVC/H.265, VP9, AV1, VVC/H.266, etc.). Compared to AVC/H.264, these codecs can either achieve the same quality besides a bitrate reduction or improve the quality while targeting the same bitrate. In this paper, we propose a Mixed-Binary Linear Programming (MBLP) model called Multi-Codec Optimization Model at the edge for Live streaming (MCOM-Live) to jointly optimize (i) the overall streaming costs, and (ii) the visual quality of the content played
out by the end-users by efficiently enabling multi-codec content delivery. Given a video content encoded with multiple codecs according to a fixed bitrate ladder, the model will choose among three available policies, i.e., fetch, transcode, or skip, the best option to handle the representations. We compare the proposed model with traditional approaches used in the industry. The experimental results show that our proposed method can reduce the additional latency by up to 23% and the streaming costs by up to 78%, besides improving the visual quality of the delivered segments by up to 0.5 dB, in terms of PSNR.

MCOM architecture overview.

Transactions on Multimedia Computing Communications and Applications (TOMM)

[PDF]

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract:

In HTTP Adaptive Streaming (HAS), videos are encoded at multiple bitrates and spatial resolutions (i.e., representations) to adapt to the heterogeneity of network conditions, device attributes, and end-user preferences. Encoding the same video segment at
multiple representations increases costs for content providers. State-of-the-art multi-encoding schemes improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to reduce the encoding time of the remaining
representations. These schemes typically use the highest bitrate representation as the reference to accelerate the encoding of the remaining representations. Nowadays, most streaming services utilize cloud-based encoding techniques, enabling a fully parallel
encoding process to reduce the overall encoding time. The highest bitrate representation has the highest encoding time than the other representations. Thus, utilizing it as the reference encoding is unfavorable in a parallel encoding setup as the overall encoding time is bound by its encoding time. This paper provides a comprehensive study of various multi-rate and multi-encoding schemes in both serial and parallel encoding scenarios. Furthermore, it introduces novel heuristics to limit the Rate Distortion Optimization (RDO) process across various representations. Based on these heuristics, three multi-encoding schemes are proposed, which rely on encoder analysis sharing across different representations: (i) optimized for the highest compression efficiency, (ii) optimized for the best compression efficiency-encoding time savings trade-off, and (iii) optimized for the best encoding time savings. Experimental results demonstrate that the proposed multi-encoding schemes (i), (ii), and (iii) reduce the overall serial encoding time by 34.71%, 45.27%, and 68.76% with a 2.3%, 3.1%, and 4.5% bitrate increase to maintain the same VMAF, respectively compared to stand-alone encodings. The overall parallel encoding time is reduced by 22.03%, 20.72%, and 76.82% compared to stand-alone encodings for schemes (i), (ii), and (iii), respectively.

An example of video representations’ storage in HAS. The input video is encoded at multiple resolutions and bitrates. Novel multi-rate and multi-resolution encoder
analysis sharing methods are presented to accelerate encoding in more than one representation.

2022 IEEE International Conference on Image Processing (ICIP)

October 16-19, 2022 | Bordeaux, France

Conference Website

Abstract: According to the Bitmovin Video Developer Report 2021, live streaming at scale has the highest scope for innovation in video streaming services. Currently, there are no open-source implementations available which can predict video complexity for live streaming applications. To this light, we plan to demo the functions of VCA software, and show accuracy of the complexities analyzed by VCA (https://vca.itec.aau.at) using the heatmaps, and show-case the speed of video complexity analysis. VCA can achieve an analysis speed of about 370fps compared to the 5fps speed of the reference SITI implementation. Hence, we show that it can be used for live streaming applications.

In the demo, we also showcase an application of VCA in detail: optimized CRF prediction for adaptive streaming, which is being presented in ICIP’22 (Paper ID: 2030). This scheme improves the compression efficiency of the conventional ABR encoding for live streaming.

Contributors:

• Vignesh V Menon, University of Klagenfurt, Austria (vignesh.menon@aau.at)
• Christian Feldmann, Bitmovin, Austria (christian.feldmann@bitmovin.com)
• Hadi Amirpour, University of Klagenfurt, Austria (hadi.amirpour@aau.at)
• Christian Timmerer, Bitmovin, Austria (christian.timmerer@bitmovin.com)

IEEE Access, A Multidisciplinary, Open-access Journal of the IEEE

[PDF]

Minh Nguyen (AAU, Austria), Daniele Lorenzi (AAU, Austria), Farzad Tashtarian (AAU, Austria), Hermann Hellwagner (AAU, Austria), Christian Timmerer (AAU, Austria)

(*) Minh Nguyen and Daniele Lorenzi contributed equally to this work

Abstract: HTTP Adaptive Streaming (HAS) solutions use various adaptive bitrate (ABR) algorithms to select suitable video qualities with the objective of coping with the variations of network connections. HTTP has been evolving with various versions and provides more and more features. Most of the existing ABR algorithms do not significantly benefit from the HTTP development when they are merely supported by the most recent HTTP version. An open research question is “How can new features of the recent HTTP versions be used to enhance the performance of HAS?” To address this question, in this paper, we introduce Days of Future Past+ (DoFP+ for short), a heuristic algorithm that takes advantage of the features of the latest HTTP version, HTTP/3, to provide high Quality of Experience (QoE) to the viewers. DoFP+ leverages HTTP/3 features, including (i) stream multiplexing, (ii) stream priority, and (iii) request cancellation to upgrade low-quality segments in the player buffer while downloading the next segment. The qualities of those segments are selected based on an objective function and throughput constraints. The objective function takes into account two factors, namely the (i) average bitrate and the (ii) video instability of the considered set of segments. We also examine different strategies of download order for those segments to optimize the QoE in limited resources scenarios. The experimental results show an improvement in QoE by up to 33% while the number of stalls and stall duration for DoFP+ are reduced by 86% and 92%, respectively, compared to state-of-the-art ABR schemes. In addition, DoFP+ saves on average up to 16% downloaded data across all test videos. Also, we find that downloading segments sequentially brings more benefits for retransmissions than concurrent downloads; and lower-quality segments should be upgraded before other segments to gain more QoE improvement. Our source code has been published for reproducibility at https://github.com/cd-athena/DoFP-Plus.

Keywords: HTTP/3, ABR algorithm, QoE, HAS, DASH