Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks

Abstract

Real-time video streaming over vehicular ad-hoc networks (VANETs) has been considered as a critical challenge for road safety applications. The purpose of this paper is to reduce the computation complexity of high efficiency video coding (HEVC) encoder for VANETs. Based on a novel spatiotemporal neighborhood set, firstly the coding tree unit depth decision algorithm is presented by controlling the depth search range. Secondly, a Bayesian classifier is used for the prediction unit decision for inter-prediction, and prior probability value is calculated by Gibbs Random Field model. Simulation results show that the overall algorithm can significantly reduce encoding time with a reasonably low loss in encoding efficiency. Compared to HEVC reference software HM16.0, the encoding time is reduced by up to 63.96%, while the Bjontegaard delta bit-rate is increased by only 0.76–0.80% on average. Moreover, the proposed HEVC encoder is low-complexity and hardware-friendly for video codecs that reside on mobile vehicles for VANETs.

JSCC-Cast: A Joint Source Channel Coding Video Encoding and Transmission System with Limited Digital Metadata

“…As stated above, the proposed JSCC-Cast system will be compared with an all-digital system based on the H.265 standard, one of the most advanced digital video codecs. However, the comparison methodology is not trivial because of the different characteristics of both schemes, but we can say that the proposed system is simpler to implement than H.265 for several reasons such as the number of signal processing tasks required by H.265 [ 47 ]. Additionally, most digital transmission systems require a feedback channel from the receiver to the sender, which is unnecessary in our proposed system.…”

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

“…There are mainly three methods for reducing coding complexity: Coding unit (CU) size and depth determination, prediction unit (PU) mode determination, transform unit (TU) size determination, and fast motion estimation (FME). For the CU size decision in inter-prediction, state-of-the-art algorithms mainly focus on the middle parameters of CU-based approaches [7,8], classification-model-based approaches [9,10], and parallel-based approaches [11].…”

Sensors, Signal and Image Processing in Biomedicine and Assisted Living

“…, robotics or vehicular technologies. In this light, a low-complexity H.265/HEVC encoder for ad-hoc vehicular networks, is presented in [ 11 ].…”

“…This special issue gathers a broad range of novel contributions on sensors, systems, and signal/image processing methods for biomedicine and assisted living. These include methods for heart, sleep and vital sign measurement [ 1 , 2 , 3 , 4 , 5 ]; human motion-related signal analysis in the context of rehabilitation and tremor assessment [ 6 , 7 , 8 ]; assistive systems for color deficient and visually challenged individuals, as well as for wheelchair control by people with motor disabilities [ 9 , 10 , 11 , 12 ]; and, image and video-based diagnostic systems [ 13 , 14 , 15 , 16 ].…”