Opening the Black Box of Learned Image Coders
Zhihao Duan, Ming Lu, Zhan Ma, Fengqing Zhu
Abstract
End-to-end learned lossy image coders (LICs), as opposed to hand-crafted image codecs, have shown increasing superiority in terms of the rate-distortion performance. However, they are mainly treated as black-box systems and their interpretability is not well studied. In this paper, we show that LICs learn a set of basis functions to transform input image for its compact representation in the latent space, as analogous to the orthogonal transforms used in image coding standards. Our analysis provides insights to help understand how learned image coders work and could benefit future design and development.
Topics & Concepts
Lossy compressionInterpretabilityComputer scienceCodecTransform codingImage (mathematics)Artificial intelligenceImage compressionComputer visionImage processingDiscrete cosine transformTelecommunicationsAdvanced Image Processing TechniquesImage and Signal Denoising MethodsAdvanced Data Compression Techniques