*2.2. Low Complexity Algorithm for H.265/HEVC Encoder*

To reduce the encoding complexity of the H.265/HEVC encoder, some fast algorithm of coding unit (CU) size decision and prediction unit (PU) mode decision is presented. In previous works, the main spatiotemporal parameters that are used for fast CU size decisions include the neighboring CU depth, rate-distortion (RD) cost, motion vector (MV), coded block flag (cbf), and sample-adaptive-offset (SAO) information. Moreover, some other statistical learning based CU selection methods are proposed include Bayesian classifier, support vector machine (SVM), decision tree (DT), AdaBoost classifier and artificial neural network (ANN).

Jiang et al. presented a fast encoding complexity method based on the probabilistic graphical model [19,20]. These proposed algorithms consist of CU early termination and CU early skip methods to reduce the redundant computing of inter-prediction in H.265/HEVC. However, these methods cannot achieve better trade-off between the encoding efficiency and encoding complexity. Refs. [21,22] focused on decreasing the CU depth to reduce the encoding complexity of the H.265/HEVC encoder. In Ref. [23], the unimodal stopping model-based early skip mode decision was used to speed up the process of mode decision. This proposed early skip mode decision method can reduce encoding time significantly. In Ref. [24], a fast algorithm for the H.265/HEVC encoder was based on the Markov Chain Monte Carlo (MCMC) model and Bayesian classifier. Even though the above fast CU size decision methods utilized the spatiotemporal correlations, the fast PU mode decision methods are ignored.

Tai et al. introduced three novel methods including early CU split, early CU termination and search range adjustment to reduce the computation complexity for H.265/HEVC [25]. This proposed algorithm can outperform previous works with respect to both the speed and the RD performance. In Ref. [26], a fast inter CU decision was proposed based on the latent sum of absolute differences (SAD) estimation. This proposed algorithm achieved an average of 52% and 58.4% reductions of the encoding time. Refs. [27,28] focused on CU size decision and PU mode decision, the fast encoding algorithms based on statistical analysis were proposed to reduce the encoding complexity for the H.265/HEVC encoder. This method can reduce about 57% and 55% of the encoding time of the H.265/HEVC encoder. The above methods can significantly reduce the encoding complexity with the joining of the CU depth and PU modes prediction, however, these previous works cannot balance the encoding complexity and encoding efficiency successfully. Moreover, the cost of hardware implementation is higher for previous works.

All in all, this paper focus on the development of video encoder that supports real-time video streaming over VANETs. We design a low-complexity and hardware-friendly encoder to allow video transmission to adapt to the VANETs environment. In addition, compared with the current literature, the proposed encoder can achieve better performance trade-off.
