1. Yimin Zhou, Hongyu Wang, Ling Tian and Ce Zhu
Temporal Correlation based Hierarchical Quantization Parameter Determination for HEVC Video Coding
Yimin Zhou, Hongyu Wang, Ling Tian and Ce Zhu
University of Electronic Science and Technology of China, Chengdu.
Introductions
To adaptively satisfy the encoding requirement of different source, the QP offset is redefined:
Finally, the QP value for picture i is determined by:
Low delay B Main
Low delay B Main HE10 Y U V
Class B
-1.02%
0.72%
-0.04%
-1.01%
0.54%
-0.22%
Class C
-0.90%
-3.34%
-3.38%
-0.98%
-3.52%
-3.69%
Class D
-0.79%
-5.25%
-4.72%
-0.70%
-5.28%
-5.80%
Class E
-2.88%
-8.01%
-7.32%
-2.82%
-8.21%
-7.72%
Overall
-1.28%
-3.43%
-3.41%
-1.26%
-3.57%
-3.89%
Class F
-2.66%
-4.70%
-3.88%
-2.73%
-4.75%
-5.58%
In video coding process, the quantization parameter (QP) [1] is adopted to quantize the residual information and compress each picture. However, in current encoders, QP is pre-configured by experience, which brings some limitations:
Lacking adaptation and scalability: no response from the video content and no feedback from previous coding results.
Cannot simultaneously handle high and low bit-rate scenarios: causing visual quality degradation.
To resolve such problems, this work proposes a temporal correlation-based adaptive QP allocation scheme.
where the function is the layer number of the coding structure for picture i, L is the maximum layer number and function,
is the adaptive adjustment factor, which is correlated by the video source temporal redundancy : motion compensated difference (MCD) and source MCD.
As MCD is hard to calculated before encoding a picture, to evaluate MCD from SMCD, this work build a linear model from simulation data:
Results
The simulation is conducted on HM 16.7 common test condition (CTC) with low-delay and random access configuration. The BD-rate result of test is as follow.
The simulation result reveals that the proposed model achieves overall BD-rate gain on all the test condition and configuration.
Random Access Main
Random Access HE10 Y U V
Class A
-1.77%
-6.66%
-8.53%
-6.80%
-8.86%
Class B
-1.02%
-8.81%
-11.27%
-1.08%
-8.96%
-11.55%
Class C
-2.16%
-6.31%
-6.59%
-2.17%
-6.79%
-6.75%
Class D
-1.40%
-7.82%
-7.51%
-1.35%
-8.13%
-7.96%
Overall
-1.52%
-7.59%
-8.65%
-1.54%
-7.87%
-8.95%
Class F
-2.42%
-5.52%
-4.68%
-2.33%
-5.58%
-5.01%
Conclusion
The adaptive QP determination scheme could evidently enhance coding performance, and our future work will be focused on performance improvement and rate-distortion optimization.
Methods
Then, the factor is calculated by the mean value and standard derivation of MCD:
The typical QP allocation model can be expressed as: