As someone who’s interested in image compression, I recently came across a fascinating Reddit post that challenged my assumptions about autoencoders. The author, who had just finished their engineering thesis, shared their findings on comparing different lossy compression methods. The results were eye-opening, and I’d love to dive deeper into what they discovered.
The author tested three methods: Principal Component Analysis (PCA), Discrete Cosine Transform (DCT) with three different masking variants, and Convolutional Autoencoders. They evaluated these methods at a 33% compression ratio on the MNIST dataset, using SSIM as the quality metric.
The results showed that autoencoders performed the best, with an SSIM of 0.97. They maintained proper digit shapes and contrast, producing the best reconstruction quality. However, the author highlighted some key limitations. Autoencoders and PCA require dataset-specific training, which limits their universality. DCT, on the other hand, works out-of-the-box but has lower quality.
What struck me was the author’s takeaway: while autoencoders performed well on this controlled dataset, their training requirement is a significant practical limitation compared to DCT’s universal applicability. This made me wonder if autoencoders are really the answer to image compression.
The author also shared some possible optimizations, such as increasing training epochs, trying different architectures, and using advanced regularization for autoencoders. They suggested keeping more principal components or DCT coefficients to trade off compression for quality.
This post got me thinking about the trade-offs in image compression. While autoencoders might produce high-quality results, their limitations can’t be ignored. Perhaps it’s time to explore other methods that can balance quality and universality.
What do you think? Have you worked with image compression before? What methods have you found to be the most effective?