**5. Experimental Results**

To evaluate the performance of the proposed low-complexity and hardware-friendly H.265/HEVC encoder for VANETs, this section shows the experimental results by implementing the proposed algorithms with the H.265/HEVC reference software [32]. The simulation environments are shown in Table 2.

**Table 2.** The simulation environments.


The Bjontegaard delta bit-rate (BDBR) is used to represent the average bit-rate [33], and the average time saving (TS) is calculated as

$$TS = \frac{1}{4} \times \sum\_{i=1}^{4} \frac{Time\_{HM16.0}(QP\_i) - Time\_{proposed}(QP\_i)}{Time\_{HM16.0}(QP\_i)} \times 100\% \tag{14}$$

where *TimeHM*16.0(*QPi*) and *Timeproposed*(*QPi*) denote the encoding time of using HM16.0 and the proposed algorithm with different QP.

In this work, the scenarios have been chosen carefully. This work focuses on the development of a video codec that supports real-time video transmission over VANETs for road safety applications. The common test conditions (CTC) are provided to conduct experiments [34]. The test sequences in CTC have different spatial and temporal characteristics and frame rates. Furthermore, the video sequences of traffic scenarios including 'Traffic' and 'BQTerrace' (as in Figure 9) are tested in this work. Moreover, we selected low delay (LD) configuration to reflect the real-time application scenario for all encoders.

(**a**) Traffic (**b**) BQTerrace

## **Figure 9.** Traffic scenario.

Tables 3 and 4 show the performance results of the CTU depth decision, PU mode decision and the overall (proposed) methods, compared to H.265/HEVC reference software in random access (RA) and low delay (LD) configurations. From the experimental results on Table 3, It can be seen that the encoding time can be reduced by 15.59%, 55.79%, and 50.96% on average for CTU depth decision, PU mode decision, and overall methods, while the BDBR can be incremented by only 0.11%, 0.96%, and 0.80%, respectively. From the experimental results in Table 4, the encoding time can be reduced by 14.05%, 50.28%, and 50.23% on average for CTU depth decision, PU mode decision, and overall methods, while the BDBR can be incremented by only 0.15%, 0.79%, and 0.76%, respectively. For high-resolution of sequences such as "BQTerrace", and "Vidyo4", the time saving is particularly high. Therefore, the overall (proposed) algorithm can significantly reduce the encoding complexity and rarely affects encoding efficiency. Moreover, the proposed method can achieve the trade-off between the encoding complexity and the encoding efficiency. In addition, the optimal tradeoff of encoding performance can be adjusted by the coupling factor *γ*. Therefore, the optimal tradeoff of encoding performance is that the encoding complexity can be reduced significantly with less than or equal to 0.8% encoding efficiency, and less low delay (LD) and random access (RA) configuration. In order to find the optimal tradeoff with coupling factor *γ*, the *γ* is set to "0.5", "0.75" and "0.85" under the same simulation environments. The compared results of the average efficiency and time saving are shown as in Table 5. From this table we can see that, in this case of *γ* = 0.75, the encoding performance is optimal in this work.




**Table 4.** Performance comparison of different parts of the proposed method (low delay (LD)).

**Table 5.** Performance comparison of different *γ*.


Video objective quality evaluation can be expressed by rate–distortion (R–D) curve. The R–D curve is fitted through four data points, and PSNR/bit-rate are assumed to be obtained for QP = 22, 27, 32, 37. In addition, when an error on the predicted depth of the current CTU occurs, the bit-rate will increase. In this paper, the video objective quality is evaluated by using bit-rate and PSNR. Then the lower the accuracies of the predicted depth of the current CTU algorithm, the more the bit-rate increases. Figure 10 shows the R–D curve of the proposed method, compared with the H.265/HEVC reference software. It can be noticed that the enlarged part of the figure shows the proposed algorithm is close to HM16.0 under the LD and RA configurations. In addition, Figure 11 shows the time saving of the sequences "Cactus" and "BlowingBubbles". It is noted that the encoding time can be reduced under different configurations.

The performance comparison of the proposed method is shown in Table 6, compared to previous works [12–14,24–26]. Goswami's work is based on Bayesian decision theory and Markov Chain Monte Carlo model (MCMC). Zhang's work is based on the Bayesian method and Conditional Random Fields (CRF). Tai's algorithm is based on depth information and RD cost. Zhu's algorithm is based on the machine learning method. Ahn's work is based on spatiotemporal encoding parameters. Xiong's work is based on the latent sum of absolute differences (SAD) estimation. However, the proposed approach is based on Bayesian rule and Gibbs Random Field. Although Zhu's method can achieve a 65.60% encoding time reduction, the BDBR is higher than the proposed method. Moreover, the increasing of the BDBR is smaller than state-of-the-art works, while the time saving is more than 50% on average. Compared with previous works [19,20], the proposed work can trade-off the encoding complexity and encoding efficiency successfully.

**Figure 10.** Rate–distortion (R–D) curve of the proposed method for "Cactus" and "BlowingBubbles".

**Figure 11.** Time savings of the proposed method for "Cactus" and "BlowingBubbles".

**Table 6.** Performance comparison of the proposed method compared to previous works.

