A Fast Depth Determination Algorithm for High Efficiency Video Coding

In this paper, a fast depth partition algorithm for High Efficiency Video Coding (HEVC) is proposed. HEVC improves the coding efficiency by using large coding units while increases the coding complexity evidently. This paper analyzes the depth correlation between adjacent frames to fast determine the depth of current coding unit. The skip mode is also adopted for better performance. Experimental results show that our algorithm can reduce 52% coding complexity compared with original HEVC algorithm. Meanwhile, the average PSNR only decreases 0.09dB and the average coding rate is decreased about 0.58%. Introduction Video coding is the process of compressing and decompressing a digital video series, which has been widely used in TV broadcasting, video conference and DVD applications [1]. The High Efficiency Video Coding (HEVC) [2] has emerged with the increasing video resolution and increasing use of parallel processing architectures, and has been established as a new video coding standard at 2010. Compared to the H.264/AVC, HEVC decreases half bit-rate while achieving similar video quality, but coding complexity increases significantly. How to decrease coding complexity and keep the image quality and the bit-rate is new challenge. In this paper, a fast inter depth determine algorithm is proposed. We analyze the correlation of the coding depth between adjacent frames. Using the previous coding depth information, skips or terminates the depth split in advance, and adopts the skip mode to decrease the coding complexity. Inter Prediction Analysis HEVC adopts an adaptive partitioning and prediction quad-tree architecture. The coding frame is split into different Coding Units (CU), and the size of CU is not fixed [3]. The quad-tree structure also makes encoding process more complex. To choose the best LCU partition, it needs to compute the rate-distortion cost of all depths, each depth needs to perform prediction, transform, quantization and entropy coding to decide whether the CU is split or not [4]. Each LCU will get the best quad-tree structure after the quad-tree traversal, as shown in Fig.1. The lower left side of Fig.1 shows the split CU structure, and the lower right side shows the quad-tree structure. As we can see from Fig.1, 64×64 LCU size needs processing prediction, transform, quantization and entropy coding. When the maximum depth is 3, one LCU needs 64 times CU traversal [5,6]. E.g. BasketballDrill, there are 104 LCUs in one frame, 6656 times CU traversal need to be done in total. And every traversal needs computing the best prediction mode, transform, quantization and entropy coding. The introduction of the more flexible partition mode and bigger coding units increases coding efficiency while increasing the computation sharply. So, it is very important to optimize the CU split [7,8]. 2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 2015) © 2015. The authors Published by Atlantis Press 1175 Figure 1 quad-tree traversal of CU partition A video consists of a series of consecutive frames. Consecutive frames differ little in temporal domain and present strong relativity[9,10]. According to the characteristic and the CU split depth structure, we find two neighbor frames have strong relativity as depicted in Figure 2. (a) the 22th frame (b) the 23th frame Figure 2 the best CU partition mode of consecutive frames Table1 consecutive frames CU depth correlation Sequence(resolution) Ratio of CU partition depth relative[%] BlowingBubbles (416×240) 90.2 BasketballDrill (832×480) 95 vidyo1 (1280×720) 97.5 ParkScene (1920×1080) 89.2 To validate the correlation of neighbor frames, a series of test is made using BlowingBubbles (416×240), BasketballDrill (832×480), vidyo1 (1080×720) and ParkScene (1920×1080). We calculate the same CU partition depth ratio between neighbor frames, but the ratio is too small. So extend the depth to present depth from minus 1 to plus 1. The result is listed in Table 1. In this