Decision Trees for Complexity Reduction in Video Compression

In their paper titled "Decision Trees for Complexity Reduction in Video Compression," Natasha Westland, André Seixas Dias, and Marta Mrak propose a method to reduce complexity in practical video encoders using multiple decision tree classifiers. The authors demonstrate the effectiveness of this approach for implementing the High Efficiency Video Coding (HEVC) standard which has high bit rate reduction capabilities but also large complexity overhead. The main source of complexity in video encoding is the optimal partitioning of each video frame into coding units (CUs), as a vast number of combinations need to be tested. To address this issue, the authors trained decision tree models to identify when the CU testing process - which involves time-consuming Lagrangian optimization - can be skipped with a high probability that the CU can remain whole. Additionally, they describe a novel approach called "manual pruning" to find the simplest and most effective decision tree model. Implementing these skip criteria reduced average encoding time by 42.1% for 17 standard test sequences in various resolutions and quantization parameters with only a Bjøntegaard Delta rate detriment of 0.7%. This approach offers an efficient solution for reducing complexity in practical video encoders while maintaining high-quality output.

Reducing Complexity in Video Compression with Decision Trees

Video compression is an important aspect of modern digital media, allowing for the efficient transmission and storage of video content. However, one major challenge faced by practitioners is the complexity associated with encoding videos. The High Efficiency Video Coding (HEVC) standard has high bit rate reduction capabilities but also large complexity overhead due to its optimal partitioning of each video frame into coding units (CUs). In their paper titled "Decision Trees for Complexity Reduction in Video Compression," Natasha Westland, André Seixas Dias, and Marta Mrak propose a method to reduce this complexity using multiple decision tree classifiers.

The Problem of Complexity in Video Encoding

The main source of complexity in video encoding is the optimal partitioning of each video frame into CUs, as a vast number of combinations need to be tested. This process involves time-consuming Lagrangian optimization which can significantly increase encoding time. To address this issue, the authors trained decision tree models to identify when the CU testing process can be skipped with a high probability that the CU can remain whole.

Decision Tree Models for Complexity Reduction

The authors describe a novel approach called "manual pruning" to find the simplest and most effective decision tree model. This approach involves removing unnecessary branches from existing trees until only those which are necessary remain - thus reducing computational cost while still maintaining accuracy. Implementing these skip criteria reduced average encoding time by 42.1% for 17 standard test sequences in various resolutions and quantization parameters with only a Bjøntegaard Delta rate detriment of 0.7%.

Conclusion

This research demonstrates how decision tree models can be used effectively to reduce complexity in practical video encoders while maintaining high-quality output. By implementing skip criteria based on manual pruning techniques, significant reductions in encoding time were achieved without sacrificing quality or performance metrics such as Bjøntegaard Delta rate detriment scores. This approach offers an efficient solution for reducing complexity during HEVC implementation and other related tasks involving video compression algorithms