How should you use and rate the quality of an image? all there is to know about it?
Regardless of where your B2B firm falls in the sales channel and who you sell to, having good quality product photos is essential to the success of both online and offline marketing campaigns.
Because online buyers cannot physically visit a store to see and touch the product they are considering purchasing in person, B2B eCommerce is more dependent on product photography than any other sales channel. Your B2B eCommerce website must therefore be able to replicate this “in-person” experience virtually utilizing images, text, and video.
Image processing algorithms go through extensive fine-tuning to produce high levels of details, improved enhancement and realism, and the perception of depth in images. These algorithms were manually configured once upon a time. The task of designing algorithms has been significantly easier with the development of autonomous image quality assessment techniques.
The difficulty of creating measurements that would record perceived image quality still exists. Take a look at two images of a tiger below as an illustration of two of the most common measures for evaluating image quality. Peak-Signal-to-Noise-Ration (PSNR) is unable to rate the image higher, which would be considered a sign of higher quality by human observers.
We begin with the criteria, or the thing that the measure is supposed to represent. Then we choose a collection of photographs to train the model on later. We gather information about the photos’ adherence to the chosen criteria by having human observers rate them. After the data is gathered, modeling can begin.
The assessment of picture impairment using deep image quality measures with full references is the topic of this paper. I will therefore only quickly discuss the pipeline’s first two steps. Understanding the decisions underlying the cutting-edge image quality evaluation models requires these as prerequisites. Since they are frequently used as the foundation for deep models, I will also briefly discuss image quality metrics that are not based on deep learning.
Participants are asked to rate each image using a single score from a predetermined scale. The choice of photos for the experiment and training process affects the results of the rating process. In contrast, ranking is significantly easier because it only considers one simple factor: which image is superior. A huge number of potential comparisons is a major disadvantage of ranking. Additionally, I’ve had experience with techniques for effective pair selection for ranking studies, and I’ve covered the overall workflow in this paper.
The following tool API yields the highest image quality:
Image Quality Checker API
You can get the image’s score by simply passing the image URL.
Additionally, the API returns “description” and “score.”
Score: a tally of points between 1 and 100. Where a quality image is better when it is closer to 1 than to 100.
“Description”: Depending on the image quality, you will receive a message as “very good,” “good,” “poor,” or “very low.”
Thanks to this API, low image quality is no longer a problem. You can use this application to assess the quality of any image you feed it. The Image Quality Checker API makes use of the industry-recognized BRISQUE no-reference picture quality score.
The best product image to display on your website’s home page can be chosen using the Image Checker Quality API. If your database contains a lot of images, you can sort the pictures by image quality. Then you can take appropriate action, such as using only high-quality photographs for your product listings or using only good quality photos for your advertisements.
Key for API Access and Authentication
A developer who registers receives a special API access key—a string of letters and numbers—that gives them access to our API endpoint. Put your bearer token in the Authorization header when using the Image Quality Checker API REST API to authenticate.