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Author

Hui Su

Bio: Hui Su is an academic researcher from Google. The author has contributed to research in topics: Codec & Data compression. The author has an hindex of 11, co-authored 33 publications receiving 480 citations. Previous affiliations of Hui Su include University of Maryland, College Park.

Papers
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Proceedings ArticleDOI
24 Jun 2018
TL;DR: A brief technical overview of key coding techniques in AV1 is provided along with preliminary compression performance comparison against VP9 and HEVC.
Abstract: AV1 is an emerging open-source and royalty-free video compression format, which is jointly developed and finalized in early 2018 by the Alliance for Open Media (AOMedia) industry consortium. The main goal of AV1 development is to achieve substantial compression gain over state-of-the-art codecs while maintaining practical decoding complexity and hardware feasibility. This paper provides a brief technical overview of key coding techniques in AV1 along with preliminary compression performance comparison against VP9 and HEVC.

260 citations

Journal ArticleDOI
26 Feb 2021
TL;DR: A technical overview of the AV1 codec design that enables the compression performance gains with considerations for hardware feasibility is provided.
Abstract: The AV1 video compression format is developed by the Alliance for Open Media consortium. It achieves more than a 30% reduction in bit rate compared to its predecessor VP9 for the same decoded video quality. This article provides a technical overview of the AV1 codec design that enables the compression performance gains with considerations for hardware feasibility.

95 citations

Proceedings ArticleDOI
29 Dec 2011
TL;DR: This paper presents a study of the video compression effect on source camera identification based on the Photo-Response Non-Uniformity (PRNU), which shows quantitatively that I-frames are more reliable than P-frames for PRNU estimation.
Abstract: This paper presents a study of the video compression effect on source camera identification based on the Photo-Response Non-Uniformity (PRNU). Specifically, the reliability of different types of frames in a compressed video is first investigated, which shows quantitatively that I-frames are more reliable than P-frames for PRNU estimation. Motivated by this observation, a new mechanism for estimating the reference PRNU and two mechanisms for estimating the test-video PRNU are proposed to achieve higher accuracy with fewer frames used. Experiments are performed to validate the effectiveness of the proposed mechanisms.

69 citations

Proceedings ArticleDOI
01 Oct 2014
TL;DR: This paper conducts a further study on the exploitation of the rolling shutter for extracting ENF traces from videos and modeled and analyzed using multirate signal processing theory.
Abstract: The electric network frequency (ENF) signal can be embedded in multimedia recordings created in areas of electrical activities. Recent work has used the ENF signal for such applications as time stamp authentication and forgery detection. It is more challenging to extract ENF signals from video recordings than from audio recordings because of the low temporal sampling rate or frame rate of video cameras. The rolling shutter of CMOS image sensor can be exploited as it exposes a frame line by line, and the effective ENF sampling rate by treating each line as a signal sample can be increased. This scheme was shown to work well with static videos. This paper conducts a further study on the exploitation of the rolling shutter for extracting ENF traces from videos. The rolling shutter mechanism is modeled and analyzed using multirate signal processing theory. Challenging cases of videos with motions are examined, and solutions to extracting ENF from them are explored.

45 citations

Journal ArticleDOI
23 Feb 2020
TL;DR: A technical overview of key coding techniques in AV1 is provided and the coding performance gains are validated by video compression tests performed with the libaom AV1 encoder against the libvpx VP9 encoder.
Abstract: In 2018, the Alliance for Open Media (AOMedia) finalized its first video compression format AV1, which is jointly developed by the industry consortium of leading video technology companies. The main goal of AV1 is to provide an open source and royalty-free video coding format that substantially outperforms state-of-the-art codecs available on the market in compression efficiency while remaining practical decoding complexity as well as being optimized for hardware feasibility and scalability on modern devices. To give detailed insights into how the targeted performance and feasibility is realized, this paper provides a technical overview of key coding techniques in AV1. Besides, the coding performance gains are validated by video compression tests performed with the libaom AV1 encoder against the libvpx VP9 encoder. Preliminary comparison with two leading HEVC encoders, x265 and HM, and the reference software of VVC is also conducted on AOM's common test set and an open 4k set.

44 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview on what has been done over the last decade in the new and emerging field of information forensics regarding theories, methodologies, state-of-the-art techniques, major applications, and to provide an outlook of the future is provided.
Abstract: In recent decades, we have witnessed the evolution of information technologies from the development of VLSI technologies, to communication and networking infrastructure, to the standardization of multimedia compression and coding schemes, to effective multimedia content search and retrieval. As a result, multimedia devices and digital content have become ubiquitous. This path of technological evolution has naturally led to a critical issue that must be addressed next, namely, to ensure that content, devices, and intellectual property are being used by authorized users for legitimate purposes, and to be able to forensically prove with high confidence when otherwise. When security is compromised, intellectual rights are violated, or authenticity is forged, forensic methodologies and tools are employed to reconstruct what has happened to digital content in order to answer who has done what, when, where, and how. The goal of this paper is to provide an overview on what has been done over the last decade in the new and emerging field of information forensics regarding theories, methodologies, state-of-the-art techniques, major applications, and to provide an outlook of the future.

340 citations

01 Dec 2015
TL;DR: TensorFlow 2.0 in ActionTensor Flow 1.x Deep Learning Cookbook machine Learning with TensorFlow, Second EditionTensor flow 2 Pocket PrimerProgramming with Tensing, Tensor Flow Machine Learning Projects, and Hands-On Neural Networks.
Abstract: TensorFlow 2.0 in ActionTensorFlow 1.x Deep Learning CookbookMachine Learning with TensorFlow 1.xMachine Learning with TensorFlow, Second EditionTensorFlow 2 Pocket PrimerProgramming with TensorFlowTensorFlow Machine Learning ProjectsHands-On Neural Networks with TensorFlow 2.0TensorFlow for Deep LearningTensor Flow Pocket PrimerNatural Language Processing with TensorFlowTensorFlow: Powerful Predictive Analytics with TensorFlowHands-On Convolutional Neural Networks with TensorFlowTensorFlow 2.0 Computer Vision CookbookIntelligent Mobile Projects with TensorFlowLearning TensorFlow.jsDeep Learning with TensorFlow 2 and KerasLearning TensorFlowTensorFlow 2 Pocket ReferenceMachine Learning Using TensorFlow CookbookTensorFlow 2.0 Quick Start GuideTensorFlow Machine Learning CookbookLearn TensorFlow 2.0Learn TensorFlow in 24 HoursHands-On Computer Vision with TensorFlow 2Mastering Computer Vision with TensorFlow 2.xPro Deep Learning with TensorFlowHands-On Machine Learning with TensorFlow.jsTensorFlow for Deep LearningTinyMLLearning TensorFlow.jsDeep Learning with TensorFlow 2 and Keras Second EditionDeep Learning with TensorFlowMastering TensorFlow 1.xAdopting TensorFlow for Real-World AITensorFlow For DummiesArtificial Intelligence with PythonHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlowLearn TensorFlow EnterpriseThe TensorFlow Workshop

306 citations

Proceedings ArticleDOI
24 Jun 2018
TL;DR: A brief technical overview of key coding techniques in AV1 is provided along with preliminary compression performance comparison against VP9 and HEVC.
Abstract: AV1 is an emerging open-source and royalty-free video compression format, which is jointly developed and finalized in early 2018 by the Alliance for Open Media (AOMedia) industry consortium. The main goal of AV1 development is to achieve substantial compression gain over state-of-the-art codecs while maintaining practical decoding complexity and hardware feasibility. This paper provides a brief technical overview of key coding techniques in AV1 along with preliminary compression performance comparison against VP9 and HEVC.

260 citations

Journal ArticleDOI
TL;DR: The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands, and can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools.
Abstract: Forensic research community keeps proposing new techniques to analyze digital images and videos. However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and contents are commonly shared through social media platforms (SMPs, for example, Facebook, YouTube, etc.). These facts pose new challenges to the forensic community: for example, most modern cameras feature digital stabilization, that is proved to severely hinder the performance of video source identification technologies; moreover, the strong re-compression enforced by SMPs during upload threatens the reliability of multimedia forensic tools. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools.

206 citations

Posted Content
TL;DR: CompressAI is presented, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs and is intended to be soon extended to the video compression domain.
Abstract: This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs. In particular, CompressAI includes pre-trained models and evaluation tools to compare learned methods with traditional codecs. Multiple models from the state-of-the-art on learned end-to-end compression have thus been reimplemented in PyTorch and trained from scratch. We also report objective comparison results using PSNR and MS-SSIM metrics vs. bit-rate, using the Kodak image dataset as test set. Although this framework currently implements models for still-picture compression, it is intended to be soon extended to the video compression domain.

175 citations