Deep Convolutional AutoEncoder-based Lossy Image Compression
This work addresses image compression efficiency for applications like storage and transmission, representing an incremental improvement over traditional methods.
The paper tackles lossy image compression by introducing a deep convolutional autoencoder architecture combined with PCA for feature rotation, achieving a 13.7% BD-rate reduction compared to JPEG2000 on the Kodak database.
Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.