**5. Experimental results**

#### **5.1 Experimental conditions**

The performance evaluation of this work is effectuated with a random access (RA) configuration through the HM 16.2 reference test model, exploiting the fast mode decision algorithms ECU, ESD, and CBF, previously detailed. To appraise the fast implementation, a comparison of HEVC encoding time, bitrate, and PSNR with the original is effectuated, where a search range is 64. Sixty-four also is the CU maximal size and CU partition depth maximal equals four. An Intel® Core TM i7– 3770 @ 3.4 GHz is used in this work with Windows 8 OS platform.

The four resolutions tested are to the four classes (class D (416 � 240), class C (832 � 480), class B (1920 � 1080), and class A (2560 � 1600)) [26]. For each video sequence, 50 is the encoded frame number used. To evaluate results, eight sequences recommended by the JCT-VC [26], each one with four quantification parameters (QP) 22, 27, 32, and 37, are used.

#### **5.2 Evaluation criteria**

To evaluate this work, we used the formula detailed in **Table 1**.

#### **5.3 Results**

**Table 2** specifies the results obtained when using the proposed fast HEVC configuration compared to the original one.

The proposed algorithm shows a time saving of up to 46.759% on average compared to the original algorithm. The speedup attains 84.51% of encoding time for BQTerrace video for QP 37. In fact, the time saving is more important for some videos such as Traffic, BQSquare, and BQTerrace sequences ranging from 57.91 to


*Where: Bitrateoriginal, PSNRoriginal and Toriginal represent bitrate, video quality, and encoding time of the original algorithm, respectively and Bitrateproposed, PSNRproposed and Tproposed, BitRateProposed represent bitrate, video quality, and encoding time of the proposed algorithm, respectively.*

**Table 1.** *Evaluation criteria.*


*Fast Motion Estimation's Configuration Using Diamond Pattern and ECU, CFM,… DOI: http://dx.doi.org/10.5772/intechopen.86792*

#### **Table 2.**

**4.3 Coded fast method (CFM)**

*Digital Imaging*

**5. Experimental results**

**5.2 Evaluation criteria**

**5.3 Results**

**Table 1.** *Evaluation criteria.*

**20**

**5.1 Experimental conditions**

The coded fast method (CFM) detects the best mode of a prediction unit [7]. As shown in **Figure 10**, for each PU mode belonging to a CU, the RD cost is calculated. An evaluation of the different coefficients, CBF for the luminance and the two chrominances, is performed. When all transform coefficients (CBF\_Y, CBF\_U, and

The performance evaluation of this work is effectuated with a random access (RA) configuration through the HM 16.2 reference test model, exploiting the fast mode decision algorithms ECU, ESD, and CBF, previously detailed. To appraise the fast implementation, a comparison of HEVC encoding time, bitrate, and PSNR with the original is effectuated, where a search range is 64. Sixty-four also is the CU maximal size and CU partition depth maximal equals four. An Intel® Core TM i7–

The four resolutions tested are to the four classes (class D (416 � 240), class C (832 � 480), class B (1920 � 1080), and class A (2560 � 1600)) [26]. For each video sequence, 50 is the encoded frame number used. To evaluate results, eight sequences recommended by the JCT-VC [26], each one with four quantification

CBF\_V) are equal to zero [9], all remaining modes will not be tested.

3770 @ 3.4 GHz is used in this work with Windows 8 OS platform.

To evaluate this work, we used the formula detailed in **Table 1**.

**Table 2** specifies the results obtained when using the proposed fast HEVC

The proposed algorithm shows a time saving of up to 46.759% on average compared to the original algorithm. The speedup attains 84.51% of encoding time for BQTerrace video for QP 37. In fact, the time saving is more important for some videos such as Traffic, BQSquare, and BQTerrace sequences ranging from 57.91 to

ΔPSNR (dB) PSNR loss Δ*PSNR* ¼ PSNR*Proposed* � PSNR*Original* ð Þ dB

*Where: Bitrateoriginal, PSNRoriginal and Toriginal represent bitrate, video quality, and encoding time of the original algorithm, respectively and Bitrateproposed, PSNRproposed and Tproposed, BitRateProposed represent bitrate, video quality,*

<sup>Δ</sup>BR (%) Bitrate increase <sup>Δ</sup>*BR*ð Þ¼ % *BitRateProposed*�*BitRateOriginal*

*TOriginal* � 100 %ð Þ

*BitRateOriginal* � 100 %ð Þ

parameters (QP) 22, 27, 32, and 37, are used.

configuration compared to the original one.

*and encoding time of the proposed algorithm, respectively.*

**Criteria Description Formula**

<sup>Δ</sup>T (%) Encoding time speedup <sup>Δ</sup>*T*ð Þ¼ % *TProposed*�*TOriginal*

*Performance evaluation of the proposed algorithm compared to the original one.*

### *Digital Imaging*

65.135%. This is due to the motion slowness in these sequences. Indeed, for videos containing low motion activities [18], the improvement is more significant. With the highest resolution, traffic video is characterized by intensive movement of objects against a stationary background. Concerning BQSquare, this video having fast motion is often coded by the bi-predictive mode, as it is the best prediction mode.

racing. Many great frequency details are in this video, since horsetail is regularly

*Fast Motion Estimation's Configuration Using Diamond Pattern and ECU, CFM,…*

The time saving is visible with 49% for BasketballDrive sequence. This video contains a high contrast and high motion activities. The background has a rather

Not only the encoding time was saved but also the bitrate which is justified by the negative values in the table, ranging from 0.002 to 2.182% for PartyScene and PeopleOnStreet with QP equal to 22 and 37, respectively. Regarding the quality of video, the PSNR deprivation is from 0.015 to 0.23 dB for BasketballDrive and

In average, the fast HEVC configuration induces a nonsignificant poverty in

**Figure 11** shows the curves of rate distortion (RD) of HEVC original algorithm and the fast one, for two sequences for each class: PeopleOnStreet and Traffic from class A (2560 1600), BQTerrace and BasketballDrive from class B (1920 1080), PartyScene and BasketballDrive from class C (832 480), and BlowingBubbles and BQSquare from class D (416 240). This can also be checked in **Table 2**. The

Four QP parameters are presented in all curves; horizontal axes on (kbps) represent the bitrate where the vertical one on (dB) represents the PSNR. **Figure 11** shows that all RD curves are overlaid [27]. In fact, the proposed changes have insignificant impairments on bitrate and PSNR. For lower QP values, the degradation is more significant. Experimental results prove that the fast configuration gives better performances than the original one, given that it offers a significant time saving, without any influence on the quality and the bitrate.

Further, for all tested sequences, an important speedup is obtained for bigger QPs. **Figure 12** evaluates the time saving in average by varying from 22 to 37. We note that the time saving increases in proportion to QP. In average, for higher QP, equal to 37, the run-time decreases by 63.5%. This decline is justified by the choice

**Table 3** summarizes the performances of the proposed work compared to dif-

*Curves of time saving for all videos coded through random access configuration with QP from 22 to 37.*

terms of video quality, around 0.106 dB, with a decrease of 0.416% in the bitrate that is a very interesting point in terms of increasing the compression

RaceHorses with QP equal to 22 and 37, respectively.

sequences are taken at QPs 22, 27, 32, and 37.

of the skip mode for bigger QP values [25].

ferent previous algorithms.

**Figure 12.**

**23**

expensive to encode.

*DOI: http://dx.doi.org/10.5772/intechopen.86792*

similar texture.

performance.

Defiantly for sequences with high activity, such as BlowingBubbles, RaceHorses, and PeopleOnStreet, the time saving is only around 34.73 and 28.38%. The worst case is for the motion-filled and dynamic RaceHorses video, which records horse

*Fast Motion Estimation's Configuration Using Diamond Pattern and ECU, CFM,… DOI: http://dx.doi.org/10.5772/intechopen.86792*

racing. Many great frequency details are in this video, since horsetail is regularly expensive to encode.

The time saving is visible with 49% for BasketballDrive sequence. This video contains a high contrast and high motion activities. The background has a rather similar texture.

Not only the encoding time was saved but also the bitrate which is justified by the negative values in the table, ranging from 0.002 to 2.182% for PartyScene and PeopleOnStreet with QP equal to 22 and 37, respectively. Regarding the quality of video, the PSNR deprivation is from 0.015 to 0.23 dB for BasketballDrive and RaceHorses with QP equal to 22 and 37, respectively.

In average, the fast HEVC configuration induces a nonsignificant poverty in terms of video quality, around 0.106 dB, with a decrease of 0.416% in the bitrate that is a very interesting point in terms of increasing the compression performance.

**Figure 11** shows the curves of rate distortion (RD) of HEVC original algorithm and the fast one, for two sequences for each class: PeopleOnStreet and Traffic from class A (2560 1600), BQTerrace and BasketballDrive from class B (1920 1080), PartyScene and BasketballDrive from class C (832 480), and BlowingBubbles and BQSquare from class D (416 240). This can also be checked in **Table 2**. The sequences are taken at QPs 22, 27, 32, and 37.

Four QP parameters are presented in all curves; horizontal axes on (kbps) represent the bitrate where the vertical one on (dB) represents the PSNR.

**Figure 11** shows that all RD curves are overlaid [27]. In fact, the proposed changes have insignificant impairments on bitrate and PSNR. For lower QP values, the degradation is more significant. Experimental results prove that the fast configuration gives better performances than the original one, given that it offers a significant time saving, without any influence on the quality and the bitrate.

Further, for all tested sequences, an important speedup is obtained for bigger QPs. **Figure 12** evaluates the time saving in average by varying from 22 to 37. We note that the time saving increases in proportion to QP. In average, for higher QP, equal to 37, the run-time decreases by 63.5%. This decline is justified by the choice of the skip mode for bigger QP values [25].

**Table 3** summarizes the performances of the proposed work compared to different previous algorithms.

#### **Figure 12.**

*Curves of time saving for all videos coded through random access configuration with QP from 22 to 37.*

65.135%. This is due to the motion slowness in these sequences. Indeed, for videos containing low motion activities [18], the improvement is more significant. With the highest resolution, traffic video is characterized by intensive movement of objects against a stationary background. Concerning BQSquare, this video having fast motion is often coded by the bi-predictive mode, as it is the best prediction

Defiantly for sequences with high activity, such as BlowingBubbles, RaceHorses, and PeopleOnStreet, the time saving is only around 34.73 and 28.38%. The worst case is for the motion-filled and dynamic RaceHorses video, which records horse

mode.

*Digital Imaging*

**Figure 11.**

**22**

*RD curve comparison of our algorithm versus the original one.*


#### **Table 3.**

*Proposed algorithm compared to previous works.*

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 the standard class video sequences (SCVS).

**Author details**

Randa Khemiri<sup>1</sup>

**25**

Fatma Elzahra Sayadi<sup>1</sup>

\*, Nejmeddine Bahri<sup>2</sup>

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

Sciences of Monastir, Monastir, Tunisia

School of Sfax, Sfax University, Tunisia

provided the original work is properly cited.

, Fatma Belghith<sup>2</sup>

, Mohamed Atri1 and Nouri Masmoudi<sup>2</sup>

1 Electronics and Microelectronics Laboratory, Monastir University, Faculty of

*Fast Motion Estimation's Configuration Using Diamond Pattern and ECU, CFM,…*

*DOI: http://dx.doi.org/10.5772/intechopen.86792*

2 Laboratory of Electronics and Information Technology, National Engineering

© 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,

, Soulef Bouaafia<sup>1</sup>

,

### **6. Conclusion**

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 terms of video quality or bitrate.

As perspectives, additional optimizations will be also implemented to reduce the encoder complexity via digital platform for video processing.

We will also exploit the fast configuration detailed in this paper for the new compression standard Joint Video Exploration Team (JVET) [28, 29].

*Fast Motion Estimation's Configuration Using Diamond Pattern and ECU, CFM,… DOI: http://dx.doi.org/10.5772/intechopen.86792*
