AI is transforming almost everything and has impacted the visual content production as well. Earlier, photography was all about lenses and sensors but, nowadays, it is more about AI-enabled processes. The same is true for photo editing services, which earlier had a manual dominance. Advances in AI has made the photo editing services to become more dependent on technology, reducing manual intervention.
What are Photo Editing services?
The journey of an image begins inside a camera, either standalone or in a mobile phone, and progresses through various levels of manipulations before it is finally rendered on billboards, magazines and most dominatingly, on a computer or a mobile screen. These manipulations can be broadly segregated into two categories:
a. Format changes
These are typically made to ensure that the images comply with bandwidth constraints in transporting the image and are delivered in a format that can be rendered for final use. These typically entail changes in the size of the image, lossy or lossless compression of the image and changes in the storage format, for example from Camera RAW format to JPEG for web display.
b. Aesthetic changes
This category of photo editing services or manipulations entails changing the nature of the image to enhance its aesthetic quality. Examples of these changes can be simple manipulations such as increasing the general brightness, adjusting for camera tilt, etc. to complex manipulations such as removal of wrinkles from clothing, changing the apparent age of a person in a photograph, object removal from a photograph of a room, etc. A number of self-use photo editing services software are available either built into applications such as Instagram and Snap or standalone to enhance a particular class of images. The photo editing can also be used by professional image editors that utilize various software to visually enhance images for their target users.
Photo editing services as an activity capture both these types of manipulations and ensures that the image best serves its intended end-use.
AI in Photo-Editing services
Given the aesthetic nature of a photo editors job, deep learning techniques are much more suitable for automation in the field than traditional algorithms. They can help the editor optimize the image for its colour and lighting condition, for example, by learning image characteristics over thousands of corrected images. The network can be trained to accommodate various parameters of the image to give a score to each image. A score being a quantifiable value makes it easy to compare with post-edited images hence start to correct images automatically. Similarly, classification algorithms help in pre-processing steps to accurately catalogue images and make them available faster for editors. Detection of objects, like bags, shoes, hats, dresses etc. and body parts like hands, nose, or postures; whether the model carrying the product, is facing towards the camera or has his or her back to it, etc. can help in processing steps such as cropping and scaling of images. To add more, tagging, naming and foldering of images can use NLP methods along with Deep Learning frameworks to help increase the efficiency of Digital Asset Management Systems.
Drivers of AI adoption in photo editing services
Market and technology factors are driving adoption of AI tools in the photo editing services. These underlying themes are discussed in this section.
a. Demand-side dynamics for AI adoption
Any product or service being marketed online needs a visual representation. Images are by far the most dominant format for conveying product features and appearance through video and 3D models are showing some traction. The tidal wave of adoption of online research and purchase has led to an explosion in the need for professionally editing imagery. More than 4 billion images are uploaded each year to online marketplaces and this is increasing by 25% each year. The sheer volume of images and the sensitivity to turn around times, the time it takes from when the product is shot to when the image is available on the marketplace, is a fundamental driver of automation in the photo editing process. Whether it be a fast-fashion catalog or a vacation rental site, each minute that an image is not available on the marketplace can mean hundreds, even thousands, of dollars in lost revenue.
b. Supply-side constraints for AI adoption
The requirements of images to be edited in large and varying volumes and with very short turnaround times places extreme constraints on in-house photo editing services teams and also outsourced agencies that provide this as a service. Large growing volumes mean that photo editors have to be constantly trained and added to the resource pool. The process has an inherent aesthetic element which requires a long training period which limits how quickly resources can become productive. The variation in volumes and rapid turnarounds result in idle capacities to be created in the supply pools which in turn impact the economics of the entire value chain. With AI-enabled automation tools, the supply pool can become much more scalable with automation helping with the peaks and troughs of the demand cycle and enabling teams to address rising volumes much more sustainably.
c. Technology factors impacting photo editing services
The rise of AI tools such as Convolution Neural Networks and scalable infrastructure provided by cloud platforms enable large scale adoption of automation tools in the Photo Editing services value chain. The ability of these new-age algorithms to recognize objects and, to some extent, contribute to standardizing aesthetic elements of an image, help reduce the overall human dependency in the process. Increased processing of images for large scale marketplaces allows for large learning data sets to be created on which these deep learning tools can be trained for improved results. Wide availability and knowledge of technology has led to several approaches to improve efficiency.
AI: the larger picture in photo editing services
Automation and Artificial Intelligence techniques present opportunities to make the entire process more consumer-driven, from capture of the image, through photo editing services and finally to display and effectiveness of the image. Automation and AI in the photo editing services and process present two unique opportunities to ensure more effective images are finally delivered:
a. Automated Content Optimization
Marketplaces often run A/B testing to gauge the effectiveness of content. Automated photo editing services would allow incremental changes to be incorporated in the aesthetic characteristics of the image and quickly run A/B testing to arrive at the optimal presentation of visuals for more effective content.
b. Improved Capture Techniques
Learnings from content optimization and effectiveness of content can be used to help direct photographers and studio management software to capture images that can result in the ideal image for a product or service. AI techniques can learn in real-time as to which characteristics of images are helping sell better and guide the capture process to highlight those aspects.
The explosive growth in the online retail of both products and services are driving fundamental shifts in how photo editing services are undertaken. The demands of online marketplaces are driving higher levels of automation in the value chain which is best addressed by employing AI, especially, Deep Learning techniques. The higher levels of automation with the use of AI also presents an opportunity to make the image creation process more consumer-driven and more sensitive to the needs of more effective content production.