A neuro-fuzzy QP estimation approach for H.266/VVC-based live video broadcasting systems.

Live video broadcasting is a popular application properly considered in lately developed standard, Versatile Video Coding (H.266/VVC). In live video broadcasting, both the bandwidth and buffer volume are limited, while a high quality level is demanded. In order to solve these problems, a Quantization Parameter Estimation Algorithm (QEA) is proposed. The core of the proposed algorithm is a neuro-fuzzy system that changes the Quantization Parameter (QP) gradually to produce a bandwidth-compliant bit rate and prohibit buffer saturation and starvation while providing high quality. The estimation is conducted according to the proportional, integral, and derivative components of the bit error. In other words, the proposed QEA is a Proportional-Integral-Derivative (PID) controller. The optimal parameters of the neuro-fuzzy system are obtained through the training process. The required data set for the training process is established by taking advantage of dynamic programming. The experiments affirm that the proposed approach achieves the target rate with an average error equal to 1.41% and fully respects the buffering boundaries. This method has at least a 2.48% bit rate reduction rather than other QEAs. Meanwhile, the proposed QEA is faster than other algorithms. [ABSTRACT FROM AUTHOR]