ICME Workshop on Hyper-Realistic Multimedia for Enhanced Quality of Experience (ICMEW)
July 18-22, 2022 | Taipei, Taiwan
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Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), and Christian Timmerer (Christian Doppler LaboratoryATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: Light field imaging enables post-capture actions such as refocusing and changing view perspective by capturing both spatial and angular information. However, capturing richer information about the 3D scene results in a huge amount of data. To improve the compression efficiency of the existing light field compression methods, we investigate the impact of light field super-resolution approaches (both spatial and angular super-resolution) on the compression efficiency. To this end, firstly, we downscale light field images over (i) spatial resolution, (ii) angular resolution, and (iii) spatial-angular resolution and encode them using Versatile Video Coding (VVC). We then apply a set of light field super-resolution deep neural networks to reconstruct light field images in their full spatial-angular resolution and compare their compression efficiency. Experimental results show that encoding the low angular resolution light field image and applying angular super-resolution yield bitrate savings of 51.16 % and 53.41 % to maintain the same PSNR and SSIM, respectively, compared to encoding the light field image in high-resolution.
Keywords: Light field, Compression, Super-resolution, VVC.


Abstract: There exist many applications that produce multimedia traffic over the Internet. Video streaming is on the list, with a rapidly growing desire for more bandwidth to deliver higher resolutions such as Ultra High Definition (UHD) 8K content. HTTP Adaptive Streaming (HAS) technique defines baselines for audio-visual content streaming to balance the delivered media quality and minimize streaming session defects. On the other hand, video codecs development and standardization help the theorem by introducing efficient algorithms and technologies. Versatile Video Coding (VVC) is one of the latest advancements in this area that is still not fully optimized and supported on all platforms. Stated optimization and supporting many platforms require years of research and development. This paper offers a dataset that facilitates the research and development of the aforementioned technologies. Our open-source dataset comprises Dynamic Adaptive Streaming over HTTP (MPEG-DASH) multimedia test assets of encoded Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC), AOMedia Video 1 (AV1), and VVC content with resolutions of up to 7680×4320 or 8K. Our dataset has a maximum media duration of 322 seconds, and we offer our MPEG-DASH packaged content with two segments lengths, 4 and 8 seconds.













