compression.ai
1. Category : Machine Learning
2. Domain : Computer Software
3. Founders : Doumet Francis, Migel Tissera
4. Established : May 30, 2018
5. Number of Employees : 1 – 10
6. Operating Status : Active
7. Funding status : Seed Funding
8. Website : compression.ai
9. Country : Vancouver, BC
10. Latest in News : Not Disclosed
Firstly we need to know how to compress your images without compromising on quality or resolution and also how to reduce storage and bandwidth costs. Faster image delivery. This can be done by compression.ai
By using compression.ai, compress your images without compromising on quality or resolution. Reduce storage and bandwidth costs. Faster image delivery. Use our API or deploy on your infrastructure.
In compression.ai, the main feature while encoding and decoding for images using machine learning that achieves on-average about 95% compression rates of a raw image without significantly losing its quality. The main components focused in this are JPEG and PNG standards with deep learning based neural networks, and the resulting outputs are better in visual quality and compression rate compared to all existing methods.
Apart from JPEG and PNG, in this we have also another field state-of-the-art ML implementation, creating our own filetype “*.mlvx” that guarantees compression below 1 bit-per-pixel whilst preserving the original quality and resolution.
This network benefits for optimized speed, Serve content anywhere with minimal load times and not only helps to resize and crop images on-the-fly with the ease of URL-encoded parameters but also serve images with dimensions and formats optimized for every browser and device.
The algorithm tailorsthe compression parameters to each and every input image. That way, images are the smallest size (in kB) they can be, while maintaining the same level of visual quality.Enhance images automatically to HDR+ quality with our machine-learned image processing algorithms and also helps to increase the resolution of images up to 4x with our machine learning based generative algorithms.
It helps in Speed up your websites and applications by lowering the image file sizes means accelerated page load times in your websites and applications. It benefits faster applications lead to higher rankings in search engine results by increasing Search Engine Optimization.
Compression.ai algorithm uses deep neural networks to create a representation of the image; we call this the Machine Learning Visual Extension and can refine their ML models by exposing them to over 10 million images on the web.
A convolutional neural network is a deep learning algorithm that can take in an input image and assigns importance (and weighting) to certain aspects of the image and helpful to reduce transmission time and costs and well as taking up less space to store.
