Computational Photography Market Analysis, Trends and 2023 Forecasts for Manufacturers

in business •  6 years ago 

computational photography.jpg[115 Pages Report] The Computational Photography Market is segmented on the basis of product into single- and dual-lens cameras, 16-lens cameras; offering into camera module and software; and application into smartphone camera, standalone camera, and machine vision. The computational photography market is expected to grow at a CAGR of 29.14% during the forecast period to reach USD 11.47 Billion by 2023 from USD 1.05 Billion in 2016. The base year used for this study is 2016, and the forecast period is between 2017 and 2023.

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Objectives of Study
• To define, describe, and forecast the computational photography market, in terms of value, segmented on the basis of type, offering, application, and geography
• To forecast the market, in terms of value, for various segments with respect to 4 main regions North America, Europe, Asia Pacific (APAC), and Rest of the World (RoW)
• To strategically analyze the micromarkets with respect to individual growth trends, prospects, and contribution to the total market
• To analyze the opportunities in the market for stakeholders and detail the competitive landscape for the market players
• To strategically profile the key players, and comprehensively analyze market rankings and core competencies
• To provide detailed information regarding the major factors influencing the growth of the market (drivers, restraints, opportunities, and industry-specific challenges)

The smartphone camera segment is expected to hold the largest share of the computational photography market by 2023. The growing consumer awareness about the high-quality smartphone cameras, along with the growing market for software-defined photography is expected to drive the market for smartphone camera.
The computational photography market for software is expected to grow at the highest CAGR between 2017 and 2023. This growth is mainly attributed to the increasing adoption of machine learning in the computational cameras. It interfaces hardware with advanced computational capabilities. Software systems enhance and extend the capabilities of devices based on the computational photography technology by compressing, expanding, and mosaicking the image. They can also remove the undesirable objects in the picture, remove camera shake, and add matt effect.

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