March 14, 2024, 4:46 a.m. | Xinjie Zhang, Shenyuan Gao, Zhening Liu, Xingtong Ge, Dailan He, Tongda Xu, Yan Wang, Jun Zhang

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.08505v1 Announce Type: cross
Abstract: Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations. However, those entropy models struggle to effectively capture the spatial-disparity characteristics inherent in stereo images, which leads to suboptimal rate-distortion results. In this paper, we propose a stereo image compression framework, named CAMSIC. CAMSIC independently transforms each image to latent representation and employs a powerful decoder-free Transformer entropy model to capture both spatial and …

abstract arxiv codec compression cs.ai cs.cv cs.mm eess.iv encode entropy however image images leads modeling paper rate results simple spatial struggle transformation transformer type

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