A neural network approach to GOPlevel rate control of x265 using Lookahead To optimize the perceived quality under a specific bitrate constraint, multipass encoding is usually performed with the rate control mode of the average bitrate (ABR) or the constant rate factor (CRF) to distribute bits as reasonably as possible in terms of perceived quality, leading to high computational complexity. In this paper, we propose to utilize the video information generated during the encoding to adaptively adjust the CRF setting at GOP level, ensuring the bits of frames in each GOP are allocated reasonably under the bitrate constraint with a singlepass encoding framework. In particular, due to the inherent relationship between CRF values and bitrates, we adopt a shallow neural network (NN) to map video content features to the CRFbitrate model. The contentrelated features are collected from the lookahead module inside the x265 encoder, including encoding cost estimation, motion vector and so on. Further, a rate control method, called content adaptive rate factor (CARF), is proposed to adjust the CRF value of each GOP with the requirement of the target bitrate by using the predicted CRF bitrate models of each GOP. The experimental results show that the proposed approach can make 84.5% testing data within 20% bitrate error (or better) and outperform the ABR mode in x265, leading to 5.23 % BDrate reduction on average.

Rate Control Based on Intermediate Description Video adaptation has been proved to be an efficient technique in dealing with various constraints such as bandwidth limitation and user requirement in multimedia applications. However, existing methods including Scalable Video Coding and transcoding cannot get a fine performance when bandwidth constraints exist in various scenarios particularly in realtime applications. In this paper, we propose a novel rate control scheme based on intermediate description. The proposed scheme can provide fast rate control for narrow and timevarying transmission channel in scenarios such as video streaming, video sharing and video on demand. In this scheme, Discrete Cosine Transform (DCT) coefficients distribution is modeled by generalized Gaussian distribution, meanwhile the parameter information of this model is stored as side information for rate control. With the stored parameter information, encoder and transcoder can achieve the target bitrate with low complexity. Furthermore, an initial Quantization Parameter (QP) determination method is also presented to calculate a proper QP for the Instantaneous Decoding Refresh (IDR) picture. Experimental results show that compared with JVTG012 in H.264, the proposed rate control scheme can save more than 85% encoding time and obtain the required bitrate more precisely, meanwhile gains a performance improvement by 0.2dB averagely.