Google uses neural networks to improve image compression
A research team at Google has developed a way to use neural networks to compress image files in a more efficient way than current methods, such as the JPEG standard. The team built an artificial intelligence system using Google's open source TensorFlow machine learning system, and then used 6 million random reference photos from the internet that had been compressed using conventional methods to train it.
The images were split into small pieces measuring 32 x 32 pixels each. The system then analyzed the 100 pieces with the least efficient compression; the idea being that it could learn from looking at the most complex areas of an image, making compression of less complex sections much easier.
After the initial training process the AI system is then able to predict how the image would look like after compression and then generates that image. What makes this method really stand out from others is that the network can intelligently decide which is the best way to compress individual areas of a given photo for the best overall result. The method still needs some work, as final results can sometimes look unpleasant to the human eye and the system are not yet capable of testing for this. Nevertheless, the project looks like an important step into the right direction and if the algorithms can be further refined you might soon be able to save even more images on your memory card or built-in device storage.
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