The computational
photography market was valued at USD 10.7 billion in 2019 and is
expected to reach USD 29.0 billion by 2024, at a CAGR of 22.0% from 2019 to
2024. The major driving factors is the trend of sharing images and videos using
advanced media sharing technologies is a major aspect of social networking on a
global basis within the smartphone and multimedia tablet ecosystem.
Also, the
enhancement in the image resolution of sensors along with the technological
advancements in camera modules, components, and design is driving the
computational photography industry. The growing demand for superior vision
technology in the computer vision industry is expected to lay new growth
opportunity for computational cameras during the forecast period.
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A few of the
emerging trends in the computational camera market include increasing
resolution and complexity of image sensors for better image quality, augmented
application of software-based imaging techniques in machine vision, growth in
4K pixel, and ultra HD technologies. Moreover, the growing quest for superior
camera technologies among leading smartphone vendors is expected to propel the
demand for computational photography technology in the smartphone segment
during the forecast period.
Based on
offering, the market for software is expected to grow at the highest CAGR. This
high growth is attributed to the use of high-end algorithms to achieve advanced
photography. Software is an essential part of computational cameras. It
interfaces hardware with advanced computational capabilities. These software
systems enhance and extend the capabilities of devices based on computational
photography by compressing, expanding, and mosaicking the image.
Based on type,
other cameras are expected to grow at the fastest rate. Other computational
cameras include cameras with 4, 8, and more lenses. Nikon has unveiled its
4-lens camera with a patent (No. 2016-114615) filed in June 2016. Other camera
manufacturers, such as Sony (Japan) and Huawei (China), are expected to enter
the computational photography market by launching cameras with variable
aperture sizes and adjustable zooming features based on the size of the image
sensor; as a result, new designs are created for multi-aperture cameras useful
in computational photography.
Based on
product, machine vision cameras are expected to grow at a significant rate
between 2019 and 2024. There is a great importance of high-quality images to
detect defects in final products. Machine vision can use computational
photography when higher quality imaging with less maintenance is desired.
Computational photography eliminates the need to adjust optics and lighting to
get a perfect image. It reduces the need for post-processing of images.
Based on
application, mixed reality is expected to grow at the highest rate. Mixed
reality is the result of blending the physical world with the digital world.
Mixed reality is the next evolution in the interaction between humans,
computers, and the environment and unlocks various possibilities that were
initially limited to our imagination. Computational photography is expected to
provide enhanced user experience with the help of advanced imaging combined
with virtual reality.
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APAC
computational photography market is expected to grow at the highest CAGR during
the forecast period. This high growth is attributed to the increasing number of
Chinese vendors such as Huawei (China), Xiaomi (China), and others. These
companies are coming up with advanced AI-based cameras for advanced imaging
purposes with powerful chipsets and processors.
In the coming
years, the robotic vision in industries is expected to use computational
cameras owing to the superior vision technology. In the coming years,
computational photography software is expected to use artificial intelligence,
which will be very useful in the AI-based machine vision technology. Movidius
(US) (acquired by Intel), launched the new Myriad 2 vision processing unit,
dedicated to imaging, The Myriad 2 has been specially made to be
power-efficient, and it will be manufactured using a 28-nanometer process,
using 12 "vision-specific vector processors.
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