Identify compression quality
using AI
Below is a free classifier to identify compression quality. Just upload your image, and our AI will predict the perceived quality of an image based on its compression level - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("compression-quality-identifier", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/compression-quality-identifier/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/compression-quality-identifier/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict the perceived quality of an image based on its compression level.
This pretrained image model uses a Nyckel-created dataset and has 10 labels, including Extreme Compression, High Compression, Lossless, Low Compression, Medium Compression, Mild Compression, No Compression, Significant Compression, Slight Compression and Very High Compression.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the perceived quality of an image based on its compression level).
Whether you're just curious or building compression quality detection into your application, we hope our classifier proves helpful.
Related Classifiers
Need to identify compression quality at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Image Library Maintenance: Organizations that manage large repositories of images can utilize the compression quality identifier to maintain optimal storage. This function helps to flag low-quality images that may need reprocessing or replacement, thus ensuring that the image library contains only high-quality assets.
- E-commerce Product Listings: Retailers can implement the identifier to assess the quality of product images uploaded to their online stores. By ensuring that only images with acceptable compression levels are displayed, businesses can enhance the visual appeal of their products, improving customer engagement and conversion rates.
- Social Media Content Moderation: Social media platforms can utilize the compression quality identifier to automatically flag user-generated images that fall below a certain quality threshold. This enables better content moderation, helping to maintain a visually appealing feed and ensuring that only high-quality images are showcased to users.
- Digital Marketing Campaigns: Marketing agencies can use the identifier to evaluate the quality of images used in digital campaigns. By identifying and improving low-quality images before distributing them, agencies can increase the effectiveness of visual marketing materials, leading to better performance metrics and ROI.
- Automated Image Optimization Tools: Software applications focused on image editing and optimization can incorporate this function to improve user experience. By automatically identifying and recommending adjustments to images with poor compression quality, these tools can empower users to produce better-quality images with minimal effort.
- Photo Sharing Platforms: Platforms that allow users to upload and share photos can implement the compression quality identifier to curate content better. By flagging and potentially rejecting low-quality uploads, the platform can enhance the overall quality of images shared, leading to a more appealing user experience.
- Healthcare Imaging Systems: Medical imaging systems can incorporate the identifier to ensure that images used for diagnosis are of adequate quality. By assessing compression levels, these systems can alert healthcare professionals to re-evaluate any low-quality images, supporting accurate diagnoses and better patient care.