Efficient Bit Rate Transcoding for High Efficiency Video Coding

Abstract

Abstract-High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.Index Terms-Bit rate adaptation, complexity scalable transcoding, high efficiency video coding (HEVC), machine learning.

Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair

“…In different works, transcoding at the cloud edge is proposed to transform the requested video to specific formats [18]. Transcoding videos at channel change events requires a lot of processing complexity and even with complexity reductions around 82% compared to reference encoders [19], such processing is excessively expensive. The only solution capable of limiting transcoding complexity with 99.2% uses coding information calculation (CIC) modules and residual encoder (RE) modules [20] such that only entropy decoding and encoding for each channel switching or packet loss event is required.…”

P-Frame Injection for Efficient Packet-Loss Repair in Ultra-Low-Latency Video Streaming

“…Error concealment [4], [5] is an example client-based solution, but as illustrated in the results section, one with significant quality decrease. Network-based techniques require undesirable computational resources in the network, such as transcoding [6]- [8] or network-distributed video coding [9], [10].…”

Analysis of Video Quality Losses in Homogeneous HEVC Video Transcoding

“…Homogenous HEVC transcoding is the subject of research works for a few years now [17][18][19][20][21][22][23][24]. The main goal of the already published works was to reduce computational complexity of HEVC re-encoding by exploiting high similarity of selected parameters of the original (input) and the transcoded bitstreams.…”