Abstract: Light field(LF) imaging captures both the positional and angular information of light rays, leading to a significant increase in LF data volume due to its high-dimensional characteristics. As a result, efficient LF compression techniques have become an important research focus in this field. In recent years, researchers have proposed various deep learningbased methods for LF compression. However, these methods often struggle to achieve endto-end joint optimization and require the explicit transmission of disparity or geometric information, which significantly increases the complexity of the coding scheme. To address this issue, this paper proposed a novel end-to-end LF compression model. The model utilized disparity relationships among LF views and used a deformable attention mechanism for disparity compensation, enabling effective LF compression by encoding and decoding disparity features and residuals. Experimental results show that the proposed method outperforms other LF compression methods in rate-distortion performance and achieves state-of-the-art performance in mid-to-high bitrate coding.

Key words: light field, light field compression, deformable attention mechanism, disparity compensation, residual coding