Computational Photography Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

The Computational Photography Market is segmented by Offerings (Camera Modules, Software), Type (Single- and Dual-Lens Cameras, 16-Lens Cameras), Application (Smartphone Cameras, Machine Vision Cameras), and Geography.

Market Snapshot

computational photography market CAGR
Study Period: 2018 - 2026
Base Year: 2020
Fastest Growing Market: Asia Pacific
Largest Market: North America
CAGR: 20 %

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Market Overview

The computational photography market is expected to hold a CAGR of 20% during the forecast period 2021 - 2026. Computational photography refers broadly to sensing strategies and algorithmic techniques that enhance or extend the capabilities of digital photography. Just as smartphone cameras rely on computational photography to adjust images despite the tiny physical lenses of a smartphone camera, so can computational photography enhance images for people with faulty vision through Augmented Reality (AR). In July 2019, Nvidia demonstrated prescription smart glasses that use augmented reality to improve a person’s vision. Instead of replacing prescription glasses every few years, augmented reality prescription eyeglasses can simply adjust the vision of each lens to adapt to a person’s changing eyesight. This implementation can inhibit high growth in the market.

  • Growing adoption of the Image Fusion Technique to achieve a high-quality image drives the market. Since image fusion techniques have been developing fast in various types of applications in recent years, methods that can assess or evaluate the performance of different fusion technologies objectively, systematically, and quantitatively have been recognized as an urgent requirement. The night color image enhancement is of great importance in both computational photography and computer vision.
  • It can effectively increase the visibility and surrealism of the scene. Also, artificial illumination light distributes unevenly at night, leading to weakening the quality of monitoring photos and increasing the difficulty of surveillance. Thus, the night color image enhancement can promote video surveillance. Currently, the main techniques for night image enhancement are image fusion and image enhancement. According to the Security Industry Association, in 2019, the video surveillance equipment market in the United States was USD 4.56 billion, with an increase of USD 1.48 billion compare to the previous year. With the integration of computational photography techniques, the market is further expected to grow.
  • Increasing demand for high-resolution computational cameras in machine vision for the autonomous vehicle sector drives the market. Major automaker companies, technology giants and specialist start-ups have invested more than USD 50 billion over the past five years, in order to develop autonomous vehicle (AV) technology. Although, Level 4 and Level 5 (as scaled by SAE) autonomous cars are unlikely to reach wide acceptance, by 2030, there would be rapid growth for Level 2 and Level 3 autonomous cars, which have advanced driver assistance systems, like collision detection, lane departure warning, and adaptive cruise control.
  • Further, an inability to handle misty driving conditions has been one of the chief obstacles to the development of autonomous vehicular navigation systems that use visible light, which is preferable to radar-based systems for their high resolution and ability to read road signs and track lane markers. MIT researchers have developed a system with the help of computational photography that could help self-driving cars see through the fog. These factors further inhibit the growth of the market.
  • With the effect of COVID-19 impact, the production of smartphones for the first half of 2020 is expected to drop drastically. The chipmaker, Qualcomm Inc., says that the coronavirus outbreak globally, it poses a potential threat to the mobile phone industry, with a possible impact on manufacturing and sales. Globally, semiconductor revenues are expected to decline by nearly 2 to 3% in 2020. Further, it disrupted the supply chain with some smartphone brands such as Sony, Samsung, who announced the integration of Qualcomm's Snapdragon 865 AI-enabled (has a camera architecture that should advance computational photography). This leads to the delay in the production as it is unpredictable currently when the pandemic gets over.

Scope of the Report

Computational photography refers to the digital image capture and processing techniques that use digital computation instead of optical processes providing its offerings in camera modules and software in the market under smartphone cameras and machine vision cameras applications.

By Offerings
Camera Modules
Software
By Type
Single- and Dual-Lens Cameras
16-Lens Cameras
By Application
Smartphone Cameras
Machine Vision Cameras
Others
Geography
North America
United States
Canada
Europe
Germany
United Kingdom
France
Rest of Europe
Asia-Pacific
India
China
Japan
Rest of Asia-Pacific
Rest of the World

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Key Market Trends

Android Smartphone to Witness Significant Market Growth

  • Cellphone photography has come a long way, starting from 0.3 megapixel VGA cameras. Over the past few years, smartphone camera technology has grown exponentially. Currently, smartphone manufacturers talk about Artificial Intelligence (AI) and machine learning for being implemented in their phones. According to Morgan Stanley, with the increasing scope of sale of the android smartphone in the coming years, the implementation of computational photography in more number of brands is highly predictable.
  • Qualcomm Spectra ISP technology combined with Computational Photography capabilities can take smartphone pictures to a whole new level of advanced imaging techniques. The next round of machine learning-added computational photography will be seen in both photos and videos.
  • At the Snapdragon Tech Summit in 2019, Qualcomm showed off a Snapdragon 865 AI-enabled “image segmentation” feature powered by Morpho software. Flagship smartphones from Huawei and Google to affordable handsets from Xiaomi and Oppo, every brand has focused on introducing some form of intelligence to help make pictures look finer.
  • Google’s Pixel 4 is an advanced example of how computational photography is driving the future of smartphone cameras. Google unveiled Pixel 4 and Pixel 4 XL, a new version of its popular smartphone, which comes in two screen sizes. While the devices include new hardware features such as an extra camera lens and an infrared face scanner to unlock the phone, Google emphasized the phones’ use of so-called computational photography, which automatically processes images to look more professional.
  • These new features provide a mode to shoot the night sky and to capture the images of stars. By adding the extra lens, Google built a software feature called Super Res Zoom, allowing users to zoom in more closely on images irrespective of losing much detail. The most significant is Android’s Night Sight features in computational photography.
  • Further, one of the earliest forms of Computational Photography introduced is HDR, a high dynamic range assisting in taking a burst of photos at different exposures and blending the best parts of them into one optimal image. Google took HDR in pushing ahead with its HDR Plus approach technology. Instead of combining photos taken at dark, ordinary, and bright exposures, it captured a larger number of dark, underexposed frames.
  • Also, triple cameras can become the future of smartphone photography. Google Pixel 5XL and Google Pixel 5 are still a year off for launch. The launch will happen in 2021. Google announced to implement machine learning and AI processing to click on amazing images with the utilization of Triple cameras in the next iteration.
  • Further, in April 2020, Microsoft announced its new Surface Duo smartphone, which will be running on an android platform whatever comes after Android 10. The device will feature a CMOS multi-sensor, 3D, IR camera that may use Computational photography algorithms.
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North America to Hold Significant Market Growth

  • The growth of the smartphone market mainly drives the progress of the computational photography market in North America. Moreover, the presence of market leaders, such as Alphabet, Apple Inc., Light, NVIDIA Corporation, and Qualcomm Inc., is also contributing significantly to the growth of the market in North America.
  • With the craze and wait of the new iPhone among the public in the United States, iPhone inhibits the growth of computational photography. In September 2019, Apple released the iPhone 11, where its Deep Fusion photography system arrived as part of Apple’s newest developer beta of iOS 13, version 13.2 beta 1. The Deep Fusion is a new image processing pipeline for medium-light images known as computational photography.
  • Deep Fusion picks the short-exposure image with the most detail and merges it with the long synthetic exposure. Unlike Smart HDR, Deep Fusion merges these two frames and is processed for noise differently than Smart HDR.
  • Further, 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. In recent years, the United States experienced rapid growth in machine vision systems, specifically in areas of advanced manufacturing.
  • According to AIA (Association for Advancing Automation), the overall North American machine vision market grew from USD1.8 billion in sales in 2010 to USD 2.07 billion till September 2019, including smart cameras sales. Further, the North American machine vision systems market is expected to grow to USD 14 billion by 2025. Driving this growth is the need for improved product inspection and quality control in the manufacturing sector, as well as the growing demand for smarter collaborative robots in manufacturing and warehousing. These factors tend to grow the market of computational photography.
  • CEVA opened a new research and development facility in Bristol, United Kingdom. The new R&D center enables CEVA to access the world-class engineering talent the city has to offer, strengthening its R&D capabilities and expediting the development of new digital signal processing and AI products.
  • Further players, such as Arm Limited, renamed its 4-TOPS Arm ML NPU as the Ethos-N77 and launched small-footprint, low-power Ethos-N57 (2-TOPS), and Ethos-N37 (1-TOPS) models for edge AI-supported with the Linux-based Arm NN SDK in October 2019. The Ethos-N37 is intended for entry-level phones and smart devices such as smart cameras and targets for computational photography applications. It is designed for smart home hubs and mainstream smartphones. This further holds the growth of the market in the coming years.
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Competitive Landscape

The computational photography market is fragmented, and the major players have used strategies such as new product launches, agreements, partnerships, acquisitions, and others to increase their footprints in this market. Key players are Alphabet Inc., Apple Inc., etc. Recent developments in the market are-

  • April 2020 - Xiaomi announced to integrate a 144-megapixel camera phone, where the predecessors of these two phones, Mi 10 Pro and Mi CC9 Pro, had 108-megapixel cameras. The phones employ computational photography and further prioritizes to improve its computational photography features.

Table of Contents

  1. 1. INTRODUCTION

    1. 1.1 Study Deliverables

    2. 1.2 Study Assumptions

    3. 1.3 Scope of the Study

  2. 2. RESEARCH METHODOLOGY

  3. 3. EXECUTIVE SUMMARY

  4. 4. MARKET DYNAMICS

    1. 4.1 Market Overview

    2. 4.2 Market Drivers

      1. 4.2.1 Growing Adoption of Image Fusion Technique to Achieve High-quality Image

      2. 4.2.2 Increasing Demand for High-resolution Computational Cameras in Machine Vision for Autonomous Vehicle

    3. 4.3 Market Restraints

      1. 4.3.1 High Manufacturing and Maintenance Costs

    4. 4.4 Industry Value Chain Analysis​

    5. 4.5 Industry Attractiveness - Porter's Five Forces Analysis​

      1. 4.5.1 Threat of New Entrants

      2. 4.5.2 Bargaining Power of Buyers/Consumers

      3. 4.5.3 Bargaining Power of Suppliers

      4. 4.5.4 Threat of Substitute Products

      5. 4.5.5 Intensity of Competitive Rivalry

    6. 4.6 Assessment of the Impact of COVID-19 on the Industry

  5. 5. MARKET SEGMENTATION

    1. 5.1 By Offerings

      1. 5.1.1 Camera Modules

      2. 5.1.2 Software

    2. 5.2 By Type

      1. 5.2.1 Single- and Dual-Lens Cameras

      2. 5.2.2 16-Lens Cameras

    3. 5.3 By Application

      1. 5.3.1 Smartphone Cameras

      2. 5.3.2 Machine Vision Cameras

      3. 5.3.3 Others

    4. 5.4 Geography

      1. 5.4.1 North America

        1. 5.4.1.1 United States

        2. 5.4.1.2 Canada

      2. 5.4.2 Europe

        1. 5.4.2.1 Germany

        2. 5.4.2.2 United Kingdom

        3. 5.4.2.3 France

        4. 5.4.2.4 Rest of Europe

      3. 5.4.3 Asia-Pacific

        1. 5.4.3.1 India

        2. 5.4.3.2 China

        3. 5.4.3.3 Japan

        4. 5.4.3.4 Rest of Asia-Pacific

      4. 5.4.4 Rest of the World

  6. 6. COMPETITIVE LANDSCAPE

    1. 6.1 Company Profiles

      1. 6.1.1 Apple Inc.

      2. 6.1.2 Alphabet Inc.

      3. 6.1.3 Qualcomm Technologies Inc.

      4. 6.1.4 Nvidia Corporation

      5. 6.1.5 Light Labs Inc.

      6. 6.1.6 CEVA Inc.

      7. 6.1.7 FotoNation Inc.

      8. 6.1.8 Algolux Inc.

      9. 6.1.9 Pelican Imaging Corporation

      10. 6.1.10 Almalence Inc.

    2. *List Not Exhaustive
  7. 7. INVESTMENT ANALYSIS

  8. 8. MARKET OPPORTUNITIES AND FUTURE TRENDS

**Subject to Availability

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Frequently Asked Questions

The Computational Photography Market market is studied from 2018 - 2026.

The Computational Photography Market is growing at a CAGR of 20% over the next 5 years.

Asia Pacific is growing at the highest CAGR over 2021- 2026.

North America holds highest share in 2020.

Apple Inc., Alphabet Inc., Qualcomm Technologies, Inc., Nvidia Corporation, Light Labs Inc. are the major companies operating in Computational Photography Market.

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