Progressive Coding for Deep Learning based Point Cloud Attribute Compression

Michael Rudolph, Aron Riemenschneider, Amr Rizk

Abstract: Progressive coding is a valuable technique for networked immersive media. As users approach objects in an immersive environment, progressive coding enables a gradual improvement of content quality. This effectively reduces bandwidth consumption compared to non-progressive methods that require to fully exchange a content representation by an independent, new representation.In this work, we introduce an approach to progressively code point cloud attributes in a learned manner by compressing quantization residuals of each preceding representation through a learned, lightweight transformation in the entropy bottleneck. This allows to progressively reduce quantization errors using a single model in an end-to-end learning manner given the quantization residuals. In contrast to the state of the art that conditions the compression on a fixed rate-distortion, i.e. it requires an ensemble of models to build an adaptive streaming system, our approach requires only a single model during compression and decompression. We present preliminary results of our method, showing bandwidth savings for the scenario of a user approaching an object and gradually transitioning from low to high quality representations.