When it comes to image compression, autoencoders seem like a promising solution. They’ve shown impressive results in reconstructing images, but are they the answer we’ve been looking for? Not quite, according to my recent engineering thesis. I compared different lossy compression methods, including principal component analysis (PCA), discrete cosine transform (DCT), and convolutional autoencoders. The results were interesting, but also highlighted some key limitations.
The autoencoders performed best, with an SSIM of 0.97, but they require dataset-specific training, which limits their universality. PCA and DCT methods, on the other hand, have lower quality results, but can be applied universally. My takeaway is that while autoencoders show promise, their practical limitations make them less than ideal for image compression.
So, what can we do differently? I’d love to hear your thoughts on alternative methods or approaches to image compression. Have you worked with autoencoders or other compression methods? What were your experiences?
If you’re interested in the technical details, I’ve written a more in-depth post with visual comparisons.