Objective

2

The multi-view video plus depth (MVD) format is gradually becoming one of the main representations for 3D videos. The 3D high-efficiency video coding (3D-HEVC) is the latest coding standard for compressing the MVD format. The 3D-HEVC inherits the coding structure of HEVC. Consequently

the splitting process of coding units (CUs) and the intra mode search process in depth map intra coding have great computational complexity. New techniques for depth map intra coding

such as depth modeling mode and simplified depth coding

have been introduced in recent years to preserve the sharp edges of depth maps. These techniques play an important role in the coding of depth maps. However

the adoption of these techniques further increases the computational complexity in 3D-HEVC encoder. A fast algorithm for depth map intra coding is proposed in this study to reduce the computational complexity of depth map intra coding.

Method

2

There are a lot of large smooth regions which are separated by sharp edges in depth maps. The CU splitting process and the rough mode decision (RMD) process are improved by the proposed algorithm by using the unique characteristics of depth maps. An algorithm based on the concept of texture primitive is proposed for the hierarchical quad-tree coding structure to early terminate the CU splitting process. First

the gradient matrix of the current CU can be calculated by using the texture analysis algorithm based on texture primitive. On the basis of statistical analysis

a strong correlation between the optimal size and sum of gradient values in gradient matrix is considered for each CU. If the sum of gradient values in gradient matrix is small

then the optimal size of current CU will be large. By contrast

if the sum of gradient values in gradient matrix is large

then the optimal size of current CU will be small. Therefore

if the sum of gradient values in gradient matrix is smaller than a given threshold

then the CU splitting process should be terminated. For the RMD process

the texture features and the smallest

$$LCRD_{\rm cost}$$

of Planar and DC are used to skip the search of angular modes in RMD for prediction units (PUs) of large size and PUs of small size

respectively. Planar and DC are two intra prediction modes that are highly suited to code smooth PUs. If the texture of current PU is flat

then Planar or DC is likely to be selected as the optimal mode. Hence

for PUs of large size

if the sum of the gradient values in the gradient matrix is zero

then only Planar and DC are added to the full-RD search list

and the RMD process is skipped. When the size of PUs is small

if the smallest

$$LCRD_{\rm cost}$$

of Planar and DC is smaller than a given threshold

then the RMD process is terminated immediately and the search of angular modes in RMD is skipped.

Results

2

In the proposed approach

the unnecessary depth levels of smooth CUs can be skipped

and the number of intra mode candidates for RMD is effectively reduced. The reference software HTM 13.0 of the 3D-HEVC standard is used to verify the coding performance of the proposed algorithm. Eight JCT-3V specified test sequences with two resolutions of 1 024×768 and 1 920×1088 are tested. The quantization parameter (QP) values for texture are 25

30

35

and 40

and the QP values for depth maps are 34

39

42

and 45. Experimental results show that compared with HTM 13.0

the proposed algorithm achieves an average depth map coding time reduction of 40.64% with a small bitrate loss of 0.17% for synthesized views under all intra scenario. For the eight test sequences

the coding time reduction of depth maps ranges from 34.77% to 51.42%

which indicates that the proposed algorithm can effectively improve encoder efficiency and has a general validity. In particular

the time saving of Poznan_Hall2 is over 50%

which is considerably larger than that of other sequences. This result is due to the fact that the depth maps of Poznan_Hall2 contain lesser edges and have a larger proportion of flat regions. The proposed algorithm also has advantages compared with other existing algorithms. A subjective quality comparison of synthesized views for Balloons (1 024×768) sequence and Poznan_Hall2 (1 920×1 088) sequence is presented to further evaluate the performance of the proposed algorithm. The results indicate that the quality of decoded synthesized views generated by the proposed algorithm is almost the same as the quality of those generated by the original HTM-13.0. The proposed algorithm can preferably preserve the edge information of depth maps.

Conclusion

2

The proposed algorithm not only accelerates quad-tree decision but also optimizes the RMD process. The algorithm periodically updates thresholds on the basis of temporal correlation of video sequences to ensure a good video quality of synthesized views. Subjective and objective evaluations show that the proposed algorithm can significantly reduce the computational complexity of depth map intra coding without decreasing the quality of synthesized views. The proposed algorithm also has practical values and can be applied to actual situations. Nonetheless

the proposed algorithm can be further improved. The algorithm optimizes the recursive splitting process of smooth CUs; however

the splitting process of CUs with a complex texture still has high computational complexity. Therefore

effective and efficient ways for reducing the depth levels of CUs with a complex texture will be studied in future research.