Content-adaptive feature-based CU size prediction for fast low-delay video encoding in HEVC

Determining the best partitioning structure of a Coding Tree Unit (CTU) is one of the most time consuming operations in HEVC encoding. Specifically, it is the evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimization that has the most significant impact on the encoding time, especially in the cases of High Definition (HD) and Ultra High Definition (UHD) videos. In order to expedite the encoding for low delay applications, this paper proposes a Coding Unit (CU) size selection and encoding algorithm for inter-prediction in the HEVC. To this end, it describes (i) two CU classification models based on Inter N×N mode motion features and RD cost thresholds to predict the CU split decision, (ii) an online training scheme for dynamic content adaptation, (iii) a motion vector reuse mechanism to expedite the motion estimation process, and finally introduces (iv) a computational complexity to coding efficiency trade-off process to enable flexible control of the algorithm. The experimental results reveal that the proposed algorithm achieves a consistent average encoding time performance ranging from 55% - 58% and 57%-61% with average Bjøntegaard Delta Bit Rate (BDBR) increases of 1.93% – 2.26% and 2.14% – 2.33% compared to the HEVC 16.0 reference software for the low delay P and low delay B configurations, respectively, across a wide range of content types and bit rates

Decoding-complexity-aware HEVC encoding using a complexity–rate–distortion model

The energy consumption of Consumer Electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device's the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper (i) first performs an analysis to determine the trade-off between the decoding complexity, video quality and bit rate with respect to a reference decoder implementation on a General Purpose Processor (GPP) architecture. Thereafter, (ii) a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity-rate-distortion optimized High Efficiency Video Coding (HEVC) bit streams. The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams

Efficient coding unit size selection based on texture analysis for HEVC intra prediction

Determining the best partitioning structure for a given Coding Tree Unit (CTU) is one of the most time consuming operations within the HEVC encoder. The brute force search through quadtree hierarchy has a significant impact on the encoding time of high definition (HD) videos. This paper presents a fast coding unit size decision-taking algorithm for intra prediction in HEVC. The proposed algorithm utilizes a low complex texture analysis technique based on the local range property of a pixel in a given neighborhood. Simulation results show that the proposed algorithm achieves an average of 72.24% encoding time efficiency improvement with similar rate distortion performance compared to HEVC reference software HM12.0 for HD videos

Effective coding unit size decision based on motion homogeneity classification for HEVC inter prediction

© 2014 IEEE. Determining the best partitioning structure for a given Coding Tree Unit (CTU) is one of the most time consuming operations within the HEVC encoder. The brute force search through quad tree hierarchy has a significant impact on the encoding time especially on high definition (HD) videos. This paper presents a fast coding unit size decision-taking algorithm for inter prediction in HEVC. The proposed algorithm uses a motion homogeneity based classification approach utilizing RD cost as a feature vector. Simulation results show that the proposed algorithm achieves an average of 73.25% encoding time efficiency improvement with similar rate distortion performance compared to HEVC HM12.0 reference software

Mechanical Metamaterials with Negative Compressibility Transitions

When tensioned, ordinary materials expand along the direction of the applied force. Here, we explore network concepts to design metamaterials exhibiting negative compressibility transitions, during which a material undergoes contraction when tensioned (or expansion when pressured). Continuous contraction of a material in the same direction of an applied tension, and in response to this tension, is inherently unstable. The conceptually similar effect we demonstrate can be achieved, however, through destabilisations of (meta)stable equilibria of the constituents. These destabilisations give rise to a stress-induced solid-solid phase transition associated with a twisted hysteresis curve for the stress-strain relationship. The strain-driven counterpart of negative compressibility transitions is a force amplification phenomenon, where an increase in deformation induces a discontinuous increase in response force. We suggest that the proposed materials could be useful for the design of actuators, force amplifiers, micro-mechanical controls, and protective devices.Comment: Supplementary information available at http://www.nature.com/nmat/journal/v11/n7/abs/nmat3331.htm

Fast coding unit size selection for HEVC inter prediction

Determining the best partitioning structure for a CTU is a time consuming operation for the HEVC encoder. This paper presents a fast CU size selection algorithm for HEVC using a CU classification technique. The proposed algorithm achieves an average of 67.83% encoding time efficiency improvement with a negligible rate-distortion loss

Content-Adaptive Feature-Based CU Size Prediction for Fast Low-Delay Video Encoding in HEVC

Determining the best partitioning structure of a Coding Tree Unit (CTU) is one of the most time consuming operations in HEVC encoding. Specifically, it is the evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimization that has the most significant impact on the encoding time, especially in the cases of High Definition (HD) and Ultra High Definition (UHD) videos. In order to expedite the encoding for low delay applications, this paper proposes a Coding Unit (CU) size selection and encoding algorithm for inter-prediction in the HEVC. To this end, it describes (i) two CU classification models based on Inter N N mode motion features and RD cost thresholds to predict the CU split decision, (ii) an online training scheme for dynamic content adaptation, (iii) a motion vector reuse mechanism to expedite the motion estimation process, and finally introduces (iv) a computational complexity to coding efficiency trade-off process to enable flexible control of the algorithm. The experimental results reveal that the proposed algorithm achieves a consistent average encoding time performance ranging from 55% – 58% and 57% – 61% with average Bjøntegaard Delta Bit Rate (BDBR) increases of 1.93% – 2.26% and 2.14% – 2.33% compared to the HEVC 16.0 reference software for the low delay P and low delay B configurations, respectively, across a wide range of content types and bit rates

CTU Level Decoder Energy Consumption Modelling for Decoder Energy-Aware HEVC Encoding

Accurate modelling of the decoding energy of a CTU is essential to determine the appropriate level of quantization required for decoder energy-aware video encoding. The proposed method predicts the number of nonzero DCT coefficients, and their energy requirements with an average accuracy of 4.8% and 11.19%, respectively