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This book provides developers, engineers, researchers and students
with detailed knowledge about the High Efficiency Video Coding
(HEVC) standard. HEVC is the successor to the widely successful
H.264/AVC video compression standard, and it provides around twice
as much compression as H.264/AVC for the same level of quality. The
applications for HEVC will not only cover the space of the
well-known current uses and capabilities of digital video – they
will also include the deployment of new services and the delivery
of enhanced video quality, such as ultra-high-definition television
(UHDTV) and video with higher dynamic range, wider range of
representable color, and greater representation precision than what
is typically found today. HEVC is the next major generation of
video coding design – a flexible, reliable and robust solution
that will support the next decade of video applications and ease
the burden of video on world-wide network traffic. This book
provides a detailed explanation of the various parts of the
standard, insight into how it was developed, and in-depth
discussion of algorithms and architectures for its implementation.
This book provides developers, engineers, researchers and
students with detailed knowledge about the High Efficiency Video
Coding (HEVC) standard. HEVC is the successor to the widely
successful H.264/AVC video compression standard, and it provides
around twice as much compression as H.264/AVC for the same level of
quality. The applications for HEVC will not only cover the space of
the well-known current uses and capabilities of digital video they
will also include the deployment of new services and the delivery
of enhanced video quality, such as ultra-high-definition television
(UHDTV) and video with higher dynamic range, wider range of
representable color, and greater representation precision than what
is typically found today. HEVC is the next major generation of
video coding design a flexible, reliable and robust solution that
will support the next decade of video applications and ease the
burden of video on world-wide network traffic. This book provides a
detailed explanation of the various parts of the standard, insight
into how it was developed, and in-depth discussion of algorithms
and architectures for its implementation."
This book provides a structured treatment of the key principles and
techniques for enabling efficient processing of deep neural
networks (DNNs). DNNs are currently widely used for many artificial
intelligence (AI) applications, including computer vision, speech
recognition, and robotics. While DNNs deliver state-of-the-art
accuracy on many AI tasks, it comes at the cost of high
computational complexity. Therefore, techniques that enable
efficient processing of deep neural networks to improve key
metrics-such as energy-efficiency, throughput, and latency-without
sacrificing accuracy or increasing hardware costs are critical to
enabling the wide deployment of DNNs in AI systems. The book
includes background on DNN processing; a description and taxonomy
of hardware architectural approaches for designing DNN
accelerators; key metrics for evaluating and comparing different
designs; features of DNN processing that are amenable to
hardware/algorithm co-design to improve energy efficiency and
throughput; and opportunities for applying new technologies.
Readers will find a structured introduction to the field as well as
formalization and organization of key concepts from contemporary
work that provide insights that may spark new ideas.
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