This paper proposes extensions of CALIC for lossless compression of light field (LF) images. The overall prediction process is improved by exploiting the linear structure of Epipolar Plane Images (EPI) in a slope based prediction scheme. The prediction is improved further by averaging predictions made using horizontal and verticals EPIs. Besides this, the difference in these predictions is included in the error energy function, and the texture context is redefined to improve the overall compression ratio. The results using the proposed method shows significant bitrate-savings in comparison to standard lossless coding schemes and offers significant reduction in computational complexity in comparison to the state-of-the-art compression schemes.
|Title of host publication||Proceedings of 2020 Data Compression Conference|
|Publication status||Published - 2020|
|Event||2020 Data Compression Conference - Virtual Conference, Snowbird, United States|
Duration: 24 Mar 2020 → 27 Mar 2020
|Conference||2020 Data Compression Conference|
|Period||24/03/2020 → 27/03/2020|
|Sponsor||Brandeis University, Microsoft USA, University of Arizona|