High Dynamic Range Video Encoding Algorithm Design and Implementation

Real world luminance ranges from a few cd/m2 at dark night to over thousands of billions cd/m2 under direct sunlight. For typical sceneries, the dynamic range which is defined as the maximum to minimum luminance ratio ranges from 3 to 10 orders of magnitudes. High Dynamic Range Imaging and Video can bring about much more realistic image to the audience. In other fields such as medical imaging (CT, MRI) or space exploration imaging, any details in the original scene have too high a price to discard and High Dynamic Range Video will be a suitable tool that can assist the development in these fields. In scene-referred capturing, we try to capture the whole range of luminance in the real scene with every possible effort to preserve visual details. Preserving every detail causes a lot of bandwidth, storage spaces; and most importantly, we will need a new video/image encoding and compressing method and system. What is lacking on the path to bring High Dynamic Range video to users is High Dynamic Range encoding systems.In this research, we have conducted analysis on the problem about the encoding and compression difficulties in High Dynamic Range Video. We have proposed a backward compatible High Dynamic Range video encoding algorithm and the respective hardware implementation. The proposed algorithm takes into account the high correlation between the normal and High Dynamic Range Image.Apart from the goal of achieving high compression ratio, other aspects such as proportionality of dynamic range versus bit-rate, flexibility for users to use different TMO, dynamic range scalability and dynamic range adjustments capabilities are considered and implemented
in our algorithm.
In conclusion, we have proposed a novel algorithm in HDR video encoding which fulfills several scalability and flexibility requirements. In the algorithm we have proposed a new HDR-LDR prediction method, a LALEMO method and a flexible automatic multi-level prediction model. Hardware implementation proves that the algorithm is realizable. We believe that with all the above virtues and benefits, the algorithm and methods proposed could become one of the important factors in bringing HDR video to real life enjoyments.