This article presents an efficient superblock-based sample adaptive offset (superSAO) that jointly exploits block-wise filter partition and probabilistic band interval segmentation to improve quality-of-experience of industrial video applications. Specifically, we investigate the partition flexibility of a superSAO block whose root size is up to 256 × 256, and propose to optimize the block-wise SAO filter by considering computation complexity and compression efficiency. Furthermore, we segment the band interval by equal probability of the sample intensity distributions, which facilitates the computation of better band offsets to attenuate ringing artifact due to quantization errors or encoded motion vectors. Experimental results show that the proposed superSAO method outperforms state-of-the-art approaches by obtaining 4.6% bandwidth reduction on average for the low delay and high-compression video applications.

A large number of industrial video applications are expected to work in real-time and power-constrained scenarios. For such applications, the coding complexity has a great impact on their performance. In this paper, we propose a complexity control method in the high-efficiency video coding intracoding to facilitate these video applications. The proposed method is performed on the coding tree unit (CTU) level, which consists of three steps, namely complexity estimation, complexity allocation, and prediction unit (PU) adaption. In the first step, a complexity estimation model is proposed to estimate the coding complexity of each CTU based on the sum of absolute transformed difference. Then, the complexity budget is allocated to each CTU proportionally to its estimated coding complexity. In PU adaption, only a subset of PU sizes are selected for the CTU according to its allocated complexity and prediction performance. A feedback-based error elimination scheme removes the complexity error during the encoding process. Experimental results show that the proposed method is able to adjust the complexity ratio from 100% to 20%. Meanwhile, the rate-distortion performance and complexity control accuracy of the proposed method are superior to those of the state-of-the-art methods.

High coding complexity is an obstruction when promoting the HEVC standard. In this paper, we propose a complexity control scheme for HEVC, which improves our previous work by employing a finer-grained complexity allocation scheme and mode sorting. In CTU-level complexity allocation, the time budget is allocated to each CTU proportionally to its estimated complexity. Then, the time budget for a CTU is further allocated to each CTU quadtree depth according to its probability of being the dominant depth. At last, a subset of modes are selected to reach the target complexity based on mode sorting. Compared with our previous work of which the least supported complexity ratio is 40%, the proposed scheme further reduces the lower bound of the ratio range to 20%.

In the latest video coding standard high efficiency video coding (HEVC), a quadtree-based coding unit (CU) partitioning scheme is adopted to better adapt to the characteristics of the video contents. However, the flexible scheme significantly increases the coding complexity, because large amount of possible CU partitioning modes should be traversed. In this paper, we propose a two-stage fast inter CU decision method to reduce the coding complexity of the HEVC encoders. In Stage I, all the CUs are classified into three categories based on the Bayesian method after the prediction unit (PU) mode merge 2N × 2N is checked. Early CU pruning and early CU skipping are then applied to two of the categories, respectively. For the remaining category which is difficult to differentiate by the rate-distortion cost of the PU mode merge 2N × 2N, an early CU pruning scheme based on conditional random fields is performed in Stage II, which takes both the local characteristics of the current CU and the coding information of its neighboring CUs into consideration. Experimental results show that our method can reduce 54.93% and 45.84% of the coding complexity on average with only 1.19% and 1.03% Bjontegaard delta bitrate increment under the random access main and the low delay P configurations, respectively.

Among the existing video-related applications, a large proportion have requirements for the scalability of the video coding complexity, such as live video chatting and video coding on power-limited mobile devices. Hence, the complexity control algorithms, which aim to make an effective and flexible tradeoff between coding complexity and rate-distortion (RD) performance, have a great practical value. In this paper, a novel complexity control scheme for high efficiency video coding (HEVC) is proposed by dynamically adjusting the depth range for each coding tree unit (CTU). To control the complexity accurately, a statistical model is proposed to estimate the coding complexity of each CTU. Then the complexity budget is allocated to each CTU proportionally to its estimated complexity. At last, the depth range is optimized for each CTU based on the allocated complexity and the probability that contains the actual maximum depth. Our method works well even if the ratio of target complexity to full complexity drops to 40%. The experimental results show that our proposed method outperforms other four state-of-the-art methods in terms of the RD performance, and has superior complexity control accuracy and complexity control stability compared with other one-pass complexity control strategies.