**Author details**

Compared to [17], the proposed work was more competent in terms of bitrate and saving time. In fact, [17] allows saving about 25.95% of encoding time with a slight bitrate. This algorithm was based on large diamond search pattern as an algorithm for motion estimation implemented on HM8.0. Concerning Liquan [19], its algorithm consists of skipping some detailed depths used in the preceding frames. This work allows saving about 21.5% of encoding time with a slight bitrate. Qin [20] implemented an algorithm established on the ECU according to a MSE adaptive threshold value. A time saving without degradation in the quality is obtained in this work. Another interesting method was presented by Podder et al. [21], where human visual features (HVF) are used for the selection of appropriate block partitioning modes. This work offered 41.44% reduction in terms of time for

**Kibeya [17] Liquan [19] Qin [20]**

**ΔBR (%)**

**ΔBR (%)**

**ΔT (%)**

**ΔT (%) ΔPSNR**

**(dB)**

**ΔBR (%)**

**ΔT (%)**

**ΔPSNR (dB)**

**ΔPSNR (dB)**

Class A �0.052 1.08 30.67 — —— 0.1% — �22.4 Class B �0.013 0.29 45.37 �0.020 0.834 �34.00 0.3% — �28.4 Class C �0.011 0.53 20.86 �0.045 1.225 �16.50 0.2% — �23.0 Class D �0.008 0.26 6.9 �0.040 1.060 �13. 50 0.2% — �17.0 Average �0.0105 0.54 25.95 �0.035 1.039 �21.33 0.2% — �22.7

HEVC induces an important progress in terms of video quality, in particular for high video resolutions. Nevertheless, this recital is combined with a bigger computational complexity which tremendously increases the encoding time. Motion estimation module using the quadtree structure represents the mainly strong process that is a conduit to the augmentation of the HEVC computational complication. In this paper to decrease this computational complexity, one fast configuration was presented to optimize the ME process by using CU partitioning fast mode decision algorithm and a diamond search. A reduction of 46.75% in the encoding time is obtained without inducing a significant degradation in encoding performance in

As perspectives, additional optimizations will be also implemented to reduce the

We will also exploit the fast configuration detailed in this paper for the new

encoder complexity via digital platform for video processing.

compression standard Joint Video Exploration Team (JVET) [28, 29].

the standard class video sequences (SCVS).

**ΔPSNR (dB)**

*Digital Imaging*

**ΔPSNR (dB)**

*Proposed algorithm compared to previous works.*

**ΔBR (%)**

**ΔBR (%)**

**ΔT (%)**

**Podder [21] Proposed fast algorithm**

**ΔT (%)**

Class A — — �41.9 �0.123 �0.478 �50.384 Class B — — �34.37 �0.059 �0.504 �55.926 Class C — — �42.92 �0.126 �0.454 �34.400 Class D — — �46.57 �0.116 �0.218 �46.326 Average — — �41.44 �0.106 �0.416 �46.759

terms of video quality or bitrate.

**6. Conclusion**

**24**

**Table 3.**

Randa Khemiri<sup>1</sup> \*, Nejmeddine Bahri<sup>2</sup> , Fatma Belghith<sup>2</sup> , Soulef Bouaafia<sup>1</sup> , Fatma Elzahra Sayadi<sup>1</sup> , Mohamed Atri1 and Nouri Masmoudi<sup>2</sup>

1 Electronics and Microelectronics Laboratory, Monastir University, Faculty of Sciences of Monastir, Monastir, Tunisia

2 Laboratory of Electronics and Information Technology, National Engineering School of Sfax, Sfax University, Tunisia

\*Address all correspondence to: randa.khemiri@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
