Conditional and Residual Methods in Scalable Coding for Humans and Machines

Autor:	de Andrade, Anderson, Harell, Alon, Foroutan, Yalda, Bajić, Ivan V.
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Information Theory Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We present methods for conditional and residual coding in the context of scalable coding for humans and machines. Our focus is on optimizing the rate-distortion performance of the reconstruction task using the information available in the computer vision task. We include an information analysis of both approaches to provide baselines and also propose an entropy model suitable for conditional coding with increased modelling capacity and similar tractability as previous work. We apply these methods to image reconstruction, using, in one instance, representations created for semantic segmentation on the Cityscapes dataset, and in another instance, representations created for object detection on the COCO dataset. In both experiments, we obtain similar performance between the conditional and residual methods, with the resulting rate-distortion curves contained within our baselines. Comment: IEEE ICME Workshop on Coding for Machines, Brisbane, Australia, 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.02562 Zobrazit plný text záznamu View this record from Arxiv