Comprehension algorithms like High Efficiency Video Coding (HEVC) facilitates fast and efficient handling of multimedia contents. Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality. However, the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content. Therefore, a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block. Normally, the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost (rate-distortion). Firstly, the proposed work utilizes neighboring blocks to extract available information for the current block. Then this information is converted to the probability that tells which intra mode might be best in the current situation. The proposed model has a strong foundation as it is based on the probability rule-2 which says that the sum of probabilities of all outcomes should be 1. Moreover, it is also based on optimal stopping theory (OST). Therefore, the proposed model performs better than many existing OST and classical secretary-based models. The proposed algorithms expedited the encoding process by 30.22% of the HEVC with 1.35% Bjontegaard Delta Bit Rate (BD-BR).

The founder of Facebook, Mark Zuckerberg, said that videos will play an important role in the future. According to online statistics, 100% of TV advertisements consist of video, around 80% of the businesses do marketing using video and 59% of the executives prefer learning from video than reading text. One more interesting fact is that around 1 billion hours of content are watched on YouTube and out of which 75% of the content is watched on mobile. This content is usually HD (High Definition) at 15–30 fps (Frames per Second) as it gives a realistic experience to the viewer. In order to support the availability of this content, the size of the video is reduced by applying compression algorithms. These compression algorithms reduce the size of the video with no loss in the subjective quality of the video.

A large number of compression standards exist and HEVC [

Before discussing the fast algorithm, let's understand the working of the HEVC to have some idea about various elements involved in the compression. For this purpose, we will use the HEVC test model (HM) software [

HEVC has many features [

The proposed work got motivated by the current trend and advancement in the field of computer vision. The statistical models proposed for computer vision improved the performance of many applications. The proposed work further explores the statistical foundation of optimal stopping theory and applies it in HEVC. In the proposed work, in order to utilize the available information in the prediction, the probability is incorporated based on the guidance-filter [

A statistical model is proposed that is based on statistic's basic principle.

The model is simple and as a result, it requires less computation.

The performance of the proposed model is better than many existing classical secretary-based models.

This article is structured as related-works, motivation, proposed-model, and results are presented in Sections 2–5, respectively. In the end, the article is concluded in Section 6.

Literature is full of fast algorithms, and one reason behind the popularity of intra mode is that it is a very challenging area. Because one mode has to be selected from 35 modes. Hence, the probability of selecting the correct option is only 0.02%.

In [

In [

The proposed work, in comparison to the state-of-the-art works presented above, tries to extract information for the current CU. In the proposed work, the probability of each intra mode is found out using the modes of the neighboring CUs because neighboring CUs and current CU has a strong correlation. This correlation is due to the existence of symmetry in natural images. The proposed work uses these probabilities of the intra modes to find out the stopping point. This early stopping will help in reducing the complexity of HEVC. The probabilities of intra modes not only act as a guide, but also help in making efficient early decisions about the intra mode. No doubt, the probability is just simple information, but it is the most relevant information too.

This section will present the motivation of doing this research. Also, we will explain how our research got inspired by the ‘guided filter’ [

The probability of any intra mode _{1}, m_{2}, …, m_{N}_{i}

The left side of

0.2 | 0.2 | 0.2 | 0.2 | 0.2 | |
---|---|---|---|---|---|

1 | 2 | 3 | 4 | 5 |

For example, consider

Kindly note that different modes are selected for the CU in HEVC and therefore, their related probabilities will be different too. Hence, sometimes this 50% success or 0.5 probability can be achieved early and sometimes it can be delayed. This process is presented in

An algorithm will be presented in this section that will achieve fast intra mode decisions. As mentioned in the motivation section that the probabilities of intra mode can be used as guidance. This guidance (probability) will only give limited information like what is the likelihood of any particular mode to be selected. The probability of any intra mode

In this section, the work mentioned in

From

Using

The simplification of

Now this concept will be further explained with the help of examples and visual evidences. Denote the left side of

0.1 | 0.1 | 0.1 | 0.1 | 0.1 | |
---|---|---|---|---|---|

5 | 2.5 | 1.6 | 1.25 | 1 | |

1.1 | 1.2 | 1.3 | 1.4 | 1.5 |

0.2 | 0.2 | 0.2 | 0.2 | 0.2 | |
---|---|---|---|---|---|

5 | 2.5 | 1.6 | 1.25 | 1 | |

1.2 | 1.4 | 1.6 | 1.8 | 2 |

0.4 | 0.2 | 0.1 | 0.05 | 0.02 | |
---|---|---|---|---|---|

1.9 | 1.2 | 1.1 | 1 | 1 | |

1.4 | 1.6 | 1.7 | 1.75 | 1.77 |

The output of

Now we discuss the third example that is given in

This section will present and discuss the encoding results of the proposed algorithm for HEVC. The limitation of this algorithm will also be presented with facts and figures. In the end, the comparison will be conducted with the existing algorithms. HM software, 16.0 version is downloaded from [

The results of the probability-based intra mode decision algorithm for the HEVC data set are presented in

Classes | Sequences | ΔP | ΔR | ΔT |
---|---|---|---|---|

A |
Nebuta | −0.04 | 0.58 | 32.83 |

Traffic | −0.08 | 1.48 | 31.08 | |

PeopleOnStreet | −0.08 | 1.48 | 31.76 | |

B |
BQTerrace | −0.08 | 1.21 | 32.16 |

Cactus | −0.05 | 1.33 | 30.8 | |

Kimono | −0.05 | 1.29 | 30.16 | |

C |
BasketballDrill | −0.06 | 1.15 | 29.43 |

RaceHorses | −0.08 | 1.28 | 30.18 | |

PartyScene | −0.12 | 1.52 | 31.77 | |

D |
BlowingBubbles | −0.08 | 1.27 | 29.13 |

BQSquare | −0.11 | 1.25 | 28.49 | |

BasketballPass | −0.08 | 1.36 | 27.23 | |

E |
KristenAndSara | −0.10 | 1.93 | 29.82 |

FourPeople | −0.10 | 1.72 | 30.43 | |

Johnny | −0.07 | 1.69 | 29.13 | |

F |
SlideShow | −0.04 | 0.88 | 29.21 |

ChinaSpeed | −0.11 | 1.25 | 30.17 | |

BasketballDrillText | −0.09 | 1.59 | 30.14 | |

Avg. | −0.08 | 1.35 | 30.22 |

The two most important, relevant and recent intra mode decision algorithms [

Classes | Sequences | [ |
[ |
Probability | ||||||
---|---|---|---|---|---|---|---|---|---|---|

ΔP | ΔR | ΔT | ΔP | ΔR | ΔT | ΔP | ΔR | ΔT | ||

A |
Traffic | −0.07 | 1.21 | 30.61 | −0.04 | 0.65 | 4.50 | −0.08 | 1.48 | 31.08 |

PeopleOnStreet | −0.07 | 1.22 | 31.20 | −0.03 | 0.45 | 5.21 | −0.08 | 1.48 | 31.76 | |

B |
Cactus | −0.04 | 1.01 | 30.52 | −0.02 | 0.59 | 3.97 | −0.05 | 1.33 | 30.80 |

Kimono | −0.03 | 0.74 | 29.34 | −0.02 | 0.61 | 2.93 | −0.05 | 1.29 | 30.16 | |

C |
RaceHorses | −0.07 | 1.04 | 30.37 | −0.03 | 0.42 | 6.19 | −0.08 | 1.28 | 30.18 |

PartyScene | −0.12 | 1.55 | 31.59 | −0.04 | 0.56 | 9.87 | −0.12 | 1.52 | 31.77 | |

D |
BQSquare | −0.12 | 1.29 | 28.57 | −0.04 | 0.39 | 8.27 | −0.11 | 1.25 | 28.49 |

BasketballPass | −0.07 | 1.14 | 26.89 | −0.02 | 0.32 | 4.36 | −0.08 | 1.36 | 27.23 | |

E |
FourPeople | −0.08 | 1.36 | 30.37 | −0.04 | 0.68 | 3.20 | −0.10 | 1.72 | 30.43 |

Johnny | −0.06 | 1.50 | 28.80 | −0.03 | 0.65 | 0.79 | −0.07 | 1.69 | 29.13 | |

F |
ChinaSpeed | −0.12 | 1.29 | 29.70 | −0.04 | 0.39 | 8.27 | −0.11 | 1.25 | 30.17 |

BasketballDrillText | −0.08 | 1.49 | 29.82 | −0.03 | 0.63 | 5.53 | −0.09 | 1.59 | 30.14 | |

Avg. | −0.08 | 1.24 | 29.82 | −0.03 | 0.53 | 5.26 | −0.08 | 1.44 | 30.11 |

Literature is full of fast intra algorithms. Out of which, some of the novel algorithms are presented in

Class | [ |
[ |
[ |
[ |
[ |
[ |
[ |
[ |
||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

ΔR | ΔT | ΔR | ΔT | ΔR | ΔT | ΔR | ΔT | ΔR | ΔT | ΔR | ΔT | ΔR | ΔT | ΔR | ΔT | ΔR | ΔT | |

A | 1.2 | 28 | 1.5 | 30 | 0.1 | 14 | −0.1 | 0.1 | 0.02 | 21 | 0.3 | 26 | −0.10 | 14 | 1.0 | 37 | 1.2 | 32 |

B | 0.9 | 25 | 1.4 | 29 | 0.1 | 15 | −0.1 | 0.4 | −0.03 | 25 | 0.4 | 27 | −0.09 | 17 | 1.0 | 37 | 1.3 | 31 |

C | 1.3 | 29 | 2.3 | 32 | 0.2 | 14 | −0.2 | 0.4 | 0.03 | 16 | 0.5 | 26 | −0.03 | 14 | 1.4 | 37 | 1.3 | 30 |

D | 1.2 | 26 | 2.7 | 30 | 0.4 | 15 | 0.0 | 0.0 | 0.19 | 18 | 0.7 | 27 | 0.01 | 6 | 2.1 | 42 | 1.3 | 28 |

E | 1.4 | 26 | 2.4 | 31 | 0.2 | 23 | −0.2 | 1.0 | 0.00 | 28 | 0.4 | 26 | 0.02 | 21 | 1.6 | 37 | 1.8 | 30 |

Avg. | 1.2 | 30 | 2.0 | 30 | 0.2 | 16 | −0.1 | 0.4 | 0.04 | 22 | 0.4 | 26 | −0.04 | 14 | 1.4 | 38 | 1.4 | 30 |

Secondly, some articles are combining early CU, early PU, early TU, and early intra-mode algorithms. Such algorithms neither fall in the fast CU category, nor fall in fast intra mode decision. Therefore, we have proposed a fast intra mode decision algorithm and compared it with only fast intra mode decision algorithms. The term

Finally, the reconstruction quality of the proposed algorithm is shown in

The intra mode computation procedure of HEVC is expedited by employing the guidance idea. The proposed algorithm proved that the algorithm that is light weight such as probability improves the performance of the fast algorithm and supports real-time applications. The proposed algorithm is applied to various statistics and HEVC examples, and its performance was satisfactory. The proposed algorithm solves the same problem as the existing works in the literature, but its methodology is very unique. Moreover, the proposed probability-based algorithm outperforms [

The authors acknowledge Dr. Junaid Ali Khan who did the proofreading of this paper.