paper-with-me

Papers

Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming

2024-08-16 · Masoumeh Farhadi Nia

With the ongoing advancement of video technology and the emergence of new video platforms, suppliers of video contents are striving to ensure that the video quality meets the desire of consumers. Accessing a limited amount of channel bandwidth, they are often looking for a novel approach to decrease the use of data and thus the required energy and cost. This study evaluates the Quality Rate performance of H.264, H.265, and VP9 codecs across resolutions (960*544, 1920*1080, 3840*2160) to optimize video quality while minimizing bitrate, crucial for energy and cost efficiency. At this approach, original videos at native resolutions were encoded, decoded, and rescaled using FFmpeg. For each resolution, encoding and decoding were performed at various quantization levels. Quality Rate (QR) curves were generated using PSNR and VMAF metric against bitrate. Convex Hull curves were then derived and mathematically modelled for each resolution. The procedure was systematically applied to H.264, H.265, and VP9 codecs. Results indicate that increasing CRF values reduce bitrate, PSNR, and VMAF, with PSNR ranging between 20-40 dB. Logarithmic polynomial modelling of convex hulls demonstrated high accuracy, with low RMSE and high R-Squared values. These findings suggest that the convex hull of one codec can predict the performance of others, aiding future content-driven prediction methodologies and enhancing adaptive streaming efficiency. Keywords: Video Codecs, Adaptive Streaming, Compression, Bitrate, PSNR, VMAF, H.264, H.265, VP9

📄 PDF Abstract BibTeX arXiv:2408.09044

Code (0)

등록된 구현이 없습니다.

Tasks

QuantizationRelation

Methods 이 논문이 사용한 방법론

CRF Conditional Random Fields or CRFs are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is…

Similar Papers 제목 키워드 기반

Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs

2022-10-02 · Angeliki Katsenou, Jongwewi Mao, Ioannis Mavromatis

The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic. The demand for higher resolutions and quality, the need for immersive video…

One Quantizer is Enough: Toward a Lightweight Audio Codec

2025-04-07 · Linwei Zhai, Han Ding, Cui Zhao, Fei Wang 외

Neural audio codecs have recently gained traction for their ability to compress high-fidelity audio and generate discrete tokens that can be utilized in downstream generative modeling tasks. However, leading approaches o…

TerraCodec: Compressing Optical Earth Observation Data

2025-10-14 · Julen Costa-Watanabe, Isabelle Wittmann, Benedikt Blumenstiel, Konrad Schindler arxiv

Earth observation (EO) satellites produce massive streams of multispectral image time series, posing pressing challenges for storage and transmission. Yet, learned EO compression remains fragmented and lacks publicly ava…

Image Compression

BVI-CC: A Dataset for Research on Video Compression and Quality Assessment

2020-03-23 · Angeliki V. Katsenou, Fan Zhang, Mariana Afonso, Goce Dimitrov 외

The video technology scenery has been very vivid over the past years, with novel video coding technologies introduced that promise improved compression performance over state-of-the-art technologies. Despite the fact tha…

Video Compression

Adapting Neural Audio Codecs to EEG

2025-11-28 · Ard Kastrati, Luca Lanzendörfer, Riccardo Rigoni, John Staib Matilla 외 arxiv

EEG and audio are inherently distinct modalities, differing in sampling rate, channel structure, and scale. Yet, we show that pretrained neural audio codecs can serve as effective starting points for EEG compression, pro…