How Video Analytic Helps to Power Broadcasting Business u Jin Huang - - PowerPoint PPT Presentation

How Video Analytic Helps to Power Broadcasting Business

u Jin Huang u CTO u Arcvideo Inc.

Agenda

• Arcvideo introduction
• How GPU been used in media processing pipeline
• How video analytic helps for broadcasting business
• Summary

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What is Arcvideo

• Arcvideo is our software defined Video Solution,

including Broadcasting Level Codec engines, Intelligent Video Analytic engines, End Device Player and Cloud Video Services

• Core products are video solution cover ingesting,

production, distribution and playback stage, handling high quality/performance Video Transcoding, Video Processing, Intelligent video analyzing, Video Streaming and Cloud Services

• Arcvideo Solutions are used to many areas, like TV

Operators, Broadcasters, Content Providers, Telecom, Enterprise, Education and others.

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Codec Video Analytic Device APP Cloud

TV Phone STB Pad PC Telecom Operator Cable Operator Mobile Operator Cloud Operator Media Companies TV Station Enterprise UGC Game Education Security Multi-Screen Transcoding Video Analytic DRM User Data Mining Content Operation Smart User Interaction Device APP & H5 CMS Monitoring Multi-CDN & QoS

GPU Advantages for Video Industry

• Multiple resources to accelerate video pipeline
• Hardware accelerated decoding and encoding

– Fast Decoding capability with good error resilience – NVENC for multiple sessions of encoding, with various quality level and latency mode choices

• CUDA accelerated video post processing

– Adaptive Deinterlacing/Frame Rate Up-conversion – Various video enhancement algorithms

• Deep Learning acceleration for intelligent

video analytic

– Face Recognition – Object Recognition

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• Less Servers and Space, save up to 80%
• Fast delivery time, up to 85% faster
• Less Power consumption, save up to 60%

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One ArcVideo GPU Server

Arcvideo Customized parts:

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• CUDA accelerated video Codec

– MPEG2 422/444, 10bit, HDR – Apple ProRes／SMPTE VC3 – Perceptual Based Coding

• Deep Learning acceleration

– Real time video quality enhancement – Face Recognition – Object Recognition, like Car and Cloth

• User interaction for live event broadcasting

– Real time VR stitching and AR rendering

• Transcoding pipeline optimization

– Seamless buffer sharing between HW Decoding, Video Processing/Analyzing and HW Encoding – Handling various streaming content dynamic change – Reduce unnecessary overhead moving uncompressed buffer

• Modified NVENC with customization of QP

– Better rate control over NVENC

• CUDA accelerated video processing and video

analytic

– Scaling/Video composition/CC/Subtitle

What Arcvideo could benefit

• Impressive video performance comparing to traditional CPU or HW multimedia solutions:

– Good Hardware accelerated Decoding/Encoding performance, and tons of CUDA cores

• Highly customizable via CUDA programming

– Easy to customize CUDA accelerated video post processing and video analytic algorithms – Flexible CUDA programming to easily fit customer request in very short time

• Balanced GPU and CUDA core configuration

– Both Tesla and GRID provide various combination of GPU and CUDA core to fit different user scenarios – Mature server vendors ecosystem to find reliable GPU servers, depends on task burden, pick multiple GPU board and achieve highest density

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How Video Analytic helps-Video Encoding

• Perceptual Video Coding
• Improve encoding video quality with same bitrate
• Video scene learning and detection to adjust encoding parameters adaptively
• Working with traditional video encoding scheme, perceptualvideo coding can

enhance compression efficiency

• HVS (human visual system) characteristicshelp to exploit perceptual

redundancy and improve video compression efficiency, but involves more computing

– Luminance, Contrast sensitivity, fovea, etc – SSIM/M-SSIM/CW-SSIM/VIF/VQM – Perceptual Noise from Spatial and Temporal Perspectives – Region of Interest

How Video Analytic helps-Video Quality Enhance

• Deep Learning for Video Quality Improvement
• Improve video quality for OTT limited

bitrate scenarios

• CNN training

– Extract highly detailed, rich color and texture blocks from high quality video frames automatically as candidate training set – Get a low resolution version as input, and

• riginal version as output

– Using lots of GPU acceleration to make it real time

How Video Analytic helps-HDR

• EOTF/OETF, Color Space conversion between SDR

and HDR or different HDR schemes

– HLG, PQ, HDR10, S-Gamut, Philips/Technicolor HDR – S-Log3, BT.709, BT.2020 – No standard way to do the conversion, proprietary tone expansion different in quality – Better Tone Mapping and Inverse Tone Mapping visual effect

• HDR characteristics help to improve encoding

quality

– Color fidelity, Adaptive Gamma Curve

How Video Analytic helps-Workflow Automation

• Improve the efficiency of workflow
• Improve efficiency of Content Production and

Management by reducing manual workloads

• Media Management

– Smart metadata extraction based on Face recognition, Object detection and recognition, Scene detection

• Content Production

– Auto channel recognition for better UX – Intelligent scene detection and video segmenting for quick editing – Fast video segment detection, including AD, Opening, Ending using video/audio fingerprint for AD Insertion and replacing – Speech to Text for auto-subtitle/cc

Summary: Why use GPU and How

• To solve computing and understanding problem

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Global Leader in Multimedia Solution

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Feedback

• Email: jhuang@arcvideo.com
• LinkedIn: huangjin.hz@gmail.com
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you by email or through the GTC Mobile App. Your feedback is important!