Towards Perceptually-Optimized Compression Of User Generated Content (UGC): Prediction Of UGC Rate-Distortion Category

Ling, Suiyi; Baveye, Yoann; Callet, Patrick Le; Skinner, Jim; Katsavounidis, Ioannis

doi:10.1109/icme46284.2020.9102752

Cited by 16 publications

(16 citation statements)

References 11 publications

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“…If the denoised signal is a better approximation to the pristine original than the UGC signal, that is, z − x < u − x , then we can use z to guide û away for u as the rate R increases. Thus, we propose to define an empirical noise encoding region using the denoised reference, (7) which can be used to choose coding parameters.…”

Section: Ugc Compression With Denoised Referencesmentioning

confidence: 99%

“…To address the issue of a low quality and unreliable reference, researchers have proposed using non reference metrics to assess subjective video quality [4,5], which can be used to perceptually optimize (guide) the compression of UGC videos. Another approach classifies UGC based on content category and similarity in ratedistortion characteristics, so that fixed coding parameters can be used for each UGC class [6,7]. While previous works have recognized that the encoding process should adapt to the quality of the input UGC video, and have provided tools and insights to design UGC compression systems, we take a step towards solving the UGC compression problem from a rate-distortion theoretic perspective [8].…”

Section: Introductionmentioning

confidence: 99%

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Compression of user generated content using denoised references

Pavéz¹,

Pérez²,

Xiong³

et al. 2022

Preprint

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Video shared over the internet is commonly referred to as user generated content (UGC). UGC video may have low quality due to various factors including previous compression. UGC video is uploaded by users, and then it is re encoded to be made available at various levels of quality and resolution. In a traditional video coding pipeline the encoder parameters are optimized to minimize a rate-distortion criteria, but when the input signal has low quality, this results in sub-optimal coding parameters optimized to preserve undesirable artifacts. In this paper we formulate the UGC compression problem as that of compression of a noisy/corrupted source. The noisy source coding theorem reveals that an optimal UGC compression system is comprised of optimal denoising of the UGC signal, followed by compression of the denoised signal. Since optimal denoising is unattainable and users may be against modification of their content, we propose using denoised references to compute distortion, so the encoding process can be guided towards perceptually better solutions. We demonstrate the effectiveness of the proposed strategy for JPEG compression of UGC images and videos.

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Section: Ugc Compression With Denoised Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compression of user generated content using denoised references

Pavéz¹,

Pérez²,

Xiong³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As shown in [7,8], general-purpose VQAs show poor performances when applied to UGC videos. Ling et al [9] exploit the rate-distortion related features to predict the ratedistortion based category of the UGC videos. The fact that various UCG distortions cause deviation in statistical properties of videos is exploited in the quality metric presented in [2].…”

Section: Related Workmentioning

confidence: 99%

A No Reference Deep Learning Based Model for Quality Assessment of UGC Videos

Lamichhane

Mazumdar

Battisti

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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Recent years have witnessed a rapid growth of user generated videos thanks to the availability of affordable video recording devices and the extreme popularity of social media platforms. Accordingly, there is a challenge for designing highly efficient video quality assessment models to monitor, control, and optimize this content. In this contribution, a novel no-reference video quality metric for user generated video is presented. It exploits the spatial and temporal information contained in the center patch of video frames and a Support Vector Regressor system for computing the objective score. Experimental results show the effectiveness of the proposed approach. To promote reproducible research and public evaluation, an implementation of RM3VQA has been made available online: https://github.com/pramitmazumdar/RM3VQA.

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“…The player still makes its own ABR decisions, but the decisions are more or less determined by the bandwidth control method on the server. To achieve this functionality, some solutions use bandwidth shaping [13], parallel multi-CDNs rules [14], or perceptually-optimized compression [15]. (III) Networkdriven Solutions.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP

Tashtarian

Bentaleb

Farahani

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Live User Generated Content (UGC) has become very popular in today's video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGC systems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA (joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an appropriate solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance of QuaLA in achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutions.

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Towards Perceptually-Optimized Compression Of User Generated Content (UGC): Prediction Of UGC Rate-Distortion Category

Cited by 16 publications

References 11 publications

Compression of user generated content using denoised references

Compression of user generated content using denoised references

A No Reference Deep Learning Based Model for Quality Assessment of UGC Videos

A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP

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