Average Encoding Time Research Articles

Learning-based CU partition prediction for fast panoramic video intra coding

In recent years, Virtual Reality (VR) technology has been applied in various aspects of life. As an indispensable component of VR, panoramic video offers viewers immersive experiences, thereby attracting widespread attention. The high resolution of panoramic video poses significant challenges for its storage and transmission, requiring efficient video compression. The Versatile Video Coding (VVC) exhibits superior coding efficiency, albeit at the expense of a several dozen-fold increase in encoding complexity. In this paper, we propose a fast Coding Unit (CU) partition algorithm for intra-coding based on Support Vector Machine (SVM), which selects appropriate features for online training of multiple classifiers based on frame texture characteristics. Additionally, in response to distortion issues in panoramic video coding, we divide video frames into different regions and adjust the Quantization Parameter (QP) values of CUs within each region to improve the encoding efficiency. Experimental results show that the proposed method achieves an average encoding time reduction of 46.21%, with an average BDBR increase of only 1.01%, achieving a good trade-off between time-saving and encoding efficiency.

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

Precise Encoding Complexity Control for Versatile Video Coding

Complexity reduction is a commonly used method to deal with complicated video coding standards, such as High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC). But the unstable performance under different video contents and Quantization Parameters (QPs) makes it difficult to precisely specify the target encoding time of every single sequence, which limits the practical use of the encoder. Inspired by rate control, in this paper, encoding time budget is regarded as a resource. We incorporate the hierarchical Group of Picture (GOP) encoding structure, the non-accelerated proportion, and block content variation within the frame, and design a top-down allocation and bottom-up feedback scheme, to achieve precise control of encoding complexity by controlling the maximum depth of QuadTree with nested Multi-type Tree (QTMT). In the scheme, Temporal ID (Tid)-based, Sum of Absolute Transformed Difference (SATD)-based methods and Linear (L) Model are designed to facilitate weighted allocation and feedback. The relationship between Planar Cost and encoding time is exploited as a Time-Cost (T-C) model, which guides the selection of Largest Coding Unit (LCU) encoding strategies in I-frames. For B-frames, we investigate a switching suppression assisted status-based method, to efficiently decide the encoding strategy of each LCU. Through the collaboration of the proposed technologies in the scheme, given any encoding time target achievable, encoding strategies will be automatically switched to help the actual encoding time gradually approach the target. To the best of our knowledge, this work is the first work on VVC-based encoding complexity control. The proposed scheme supports directly specifying the target encoding time or target Frame Per Second (FPS), and accurately realizing it within one pass. According to experimental results, under the target encoding time ratio of 80%, 60% and 40%, the average encoding time error is kept under 0.24%, 0.03% and 0.02% by our method, outperforming state-of-the-art works base on HEVC. These results prove the advantage of the proposed scheme.

DECISION ALGORITHM FOR INTRA PREDICTION IN HIGH-EFFICIENCY VIDEO CODING (HEVC)

Prediction in HEVC exploits the redundant information in the fame to improve compression efficiency. The computational complexity of prediction is comparatively high as it recursively calculates the depth by comparing the rate-distortion optimization cost (RDO) exhaustively. The deep learning technology has shown a good mark in this area compared to traditional signal processing because of its content-based analysis and learning ability. This paper proposes a deep depth decision algorithm to predict the depth of the coding tree unit (CTU) and store it as a 16-element vector, and this model is pipelined to the HEVC encoder to compare the time taken and bit rate of encoding. The comparison chart clearly shows the reduction in computational time and enhancement in bitrate while encoding. The dataset used here is generated for the model with 110000 frames of the various resolutions, split into test, training, and validation, and trained on a depth decision model. The trained model interfaced with the HEVC encoder is compared with the normal encoder. The evaluation is done for quality check for the proposed model with BD-PSNR and BD-Bitrate shows a dip of 0.6 in BD-PSNR and increment of 6.7 in BD-Bitrate. When pipelined with the original HEVC, the RDO cost shows an improvement over existing techniques. The average encoding time is reduced by about 72% by the pipelined deep depth decision algorithm that points to the reduction in computational complexity. An average time saving of 88.49% is achieved with a deep depth decision algorithm-based encoder compared to the existing techniques.

Improved inter-view correlations for low complexity MV-HEVC

The more advanced multi-view extension, MV-HEVC, effectively exploits visual similarities between multi-view videos and enables high compression efficiency. Each view in the multi-view sequence depends on the captured scene, the distance between cameras and recording angles. Increasing the distance between dependent viewpoints generates an inter-view disparity. This impacts the inter-view similarities, affects the disparity estimation and further increases the computational complexity of the MV-HEVC encoder. In this paper, an efficient earlier disparity estimation is proposed for low complexity MV-HEVC. This algorithm is based on reducing the complexity of disparity estimation by eliminating the inter-view offset. Moreover, the inter-view similarities are controlled by considering the reliability of each coding unit size in the search range. This reliability is estimated by reducing the number of searching points within a new limited window. For reliable motion estimation, we further proposed an earlier decision of coding units splitting in the dependent views according to those in the reference views. Experimental results show that the proposed algorithm can achieve an average encoding time saving of 20.37%–40,61% with marginal performance degradation.

Machine Learning-Based Early Skip Decision for Intra Subpartition Prediction in VVC

The recently published video coding standard, Versatile Video Coding (VVC/H.266), has the intra subpartition (ISP) coding mode, which divides an intra-predicted block into smaller blocks called subpartitions, each of which can be predicted using the newly reconstructed subpartition while still sharing the same intra mode. It is a VVC intra prediction tool that brings significant coding gains but also increases its encoding complexity. In this context, this paper addresses how to speed up the ISP encoding process by designing an ISP early skip decision scheme using a simple LightGBM model. The proposed ISP decision expedites the encoding process by early determination of whether or not to skip the ISP mode test. The proposed method uses the mean absolute sum of transform coefficients as a key feature. Our experimental results show an average encoding time saving of 7.2% under the all intra coding configuration with 0.08% BDBR loss. Compared to the state-of-the-art methods, our solution is able to outperform related works in terms of the combined rate-distortion and time saving.

A Tamper-Resistant Algorithm Using Blockchain for the Digital Tachograph

A tachograph in a vehicle records the vehicle operating conditions, such as speed, distance, brake operation conditions, acceleration, GPS information, etc., in intervals of one second. For accidents, the tachograph records information, such as the acceleration and direction of a vehicle traveling in intervals of 1/100 s for 10 s before and after the accident occurs as collision data. A vehicle equipped with a tachograph is obliged to upload operation data to administrative organizations periodically via other auxiliary storage devices like a USB attached external memory or online wireless communication. If there is a problem with the recorded contents, data may be at risk of being tampered with during the uploading process. This research proposed tamper-resistant technology based on blockchain for data in online and offline environments. The suggested algorithm proposed a new data recording mechanism that operates in low-level hardware of digital tachographs for tamper-resistance in light blockchains and on/offline situations. The average encoding time of the proposed light blockchain was 1.85 ms/Mb, while the average decoding time was 1.65 ms/Mb. With the outliers in statistical tests removed, the estimated average encoding and decoding time was 1.32 ms/Mb and 1.29 ms/Mb, respectively, and the tamper verification test detected all the tampered data.

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

Symmetry considerations play a key role in modern science, and any differentiable symmetry of the action of a physical system has a corresponding conservation law. Symmetry may be regarded as reduction of Entropy. This work focuses on reducing the computational complexity of modern video coding standards by using the maximum entropy principle. The high computational complexity of the coding unit (CU) size decision in modern video coding standards is a critical challenge for real-time applications. This problem is solved in a novel approach considering CU termination, skip, and normal decisions as three-class making problems. The maximum entropy model (MEM) is formulated to the CU size decision problem, which can optimize the conditional entropy; the improved iterative scaling (IIS) algorithm is used to solve this optimization problem. The classification features consist of the spatio-temporal information of the CU, including the rate–distortion (RD) cost, coded block flag (CBF), and depth. For the case analysis, the proposed method is based on High Efficiency Video Coding (H.265/HEVC) standards. The experimental results demonstrate that the proposed method can reduce the computational complexity of the H.265/HEVC encoder significantly. Compared with the H.265/HEVC reference model, the proposed method can reduce the average encoding time by 53.27% and 56.36% under low delay and random access configurations, while Bjontegaard Delta Bit Rates (BD-BRs) are 0.72% and 0.93% on average.

ICUS: Intelligent CU Size Selection for HEVC Inter Prediction

The hierarchical quadtree partitioning of Coding Tree Units (CTU) is one of the striking features in HEVC that contributes towards its superior coding performance over its predecessors. However, the brute force evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimisation, to determine the best partitioning structure for a given content, makes it one of the most time-consuming operations in HEVC encoding. In this context, this paper proposes an intelligent fast Coding Unit (CU) size selection algorithm to expedite the encoding process of HEVC inter-prediction. The proposed algorithm introduces (i) two CU split likelihood modelling and classification approaches using Support Vector Machines (SVM) and Bayesian probabilistic models, and (ii) a fast CU selection algorithm that makes use of both offline trained SVMs and online trained Bayesian probabilistic models. Finally, (iii) a computational complexity to coding efficiency trade-off mechanism is introduced to flexibly control the algorithm to suit different encoding requirements. The experimental results of the proposed algorithm demonstrate an average encoding time reduction performance of 53.46%, 61.15%, and 58.15% for Low Delay B , Random Access , and Low Delay P configurations, respectively, with Bjontegaard Delta-Bit Rate (BD-BR) losses of 2.35%, 2.9%, and 2.35%, respectively, when evaluated across a wide range of content types and quality levels.

Multi-level rate-constrained successive elimination algorithm tailored to suboptimal motion estimation in HEVC

In the context of motion estimation for video coding, successive elimination algorithms (SEAs) significantly reduce the number of candidates evaluated during motion estimation without altering the resulting optimal motion vector. Nevertheless, SEA is often only used in conjunction with exhaustive search algorithms (e.g., full search). In this paper, we combine the multi-level successive elimination algorithm (ML-SEA) and the rate-constrained successive elimination algorithm (RCSEA) and show that they can be advantageously applied to suboptimal search algorithms. We demonstrate that the savings brought about by the new multi-level RCSEA (ML-RCSEA) outweigh the pre-computational costs of this approach for the Test Zonal (TZ) Search algorithm found in the HM reference encoder. We propose a novel multi-level composition pattern for performing RCSEA on an asymmetric partitioning. We introduce a double-check mechanism for RCSEA, and show that on average, it avoids computing 71% of motion vector (MV) costs. We also apply the proposed ML-RCSEA to bi-predictive refinement search and leverage a cost-based search ordering to remove 56% of error metric computations, on average. When compared to the HM reference encoder, our experiments show that the proposed solution reduces the TZ Search time by approximately 45%, contributing to an average encoding time reduction of about 7%, without increasing the Bjøntegaard delta rate (BD-Rate).

Mode Skipping for HEVC Screen Content Coding via Random Forest

Screen content coding (SCC) is the extension to high-efficiency video coding (HEVC) for compressing screen content videos. New coding tools, intrablock copy (IBC), and palette (PLT) modes, are introduced to encode screen content (SC) such as texts and graphics. The IBC mode is used for encoding repeating patterns by performing block matching within the same frame, while the PLT mode is designed for SC with few distinct colors by coding the major colors and their corresponding locations using an index map. However, the use of IBC and PLT modes increases the encoder complexity remarkably though coding efficiency can be improved. Therefore, we propose to have a mode skipping approach to reduce the encoder complexity of SCC by making use of SC characteristics, neighbor coding unit (CU) correlations, and intermediate cost information via random forest (RF). Detailed feature analyses and sample preparation are also described. A novel hyperparameter tuning approach with the consideration of coding bitrate and encoding time is proposed for RFs at each CU size to further boost the encoding process. Experimental results show that our proposed approach can obtain 45.06% average encoding time reduction with only a 1.08% increase in Bjontegaard delta bitrate. Average encoding time can even be reduced to 58.57% by regulating the hyperparameters.

H.265 Intra-Picture Prediction Acceleration using Low Complexity Model

H.265 also called High Efficiency Video Coding is the new futuristic international standard proposed by Joint collaboration Team on Video Coding and released in 2013 in the view of constantly increasing demand of video applications. This new standard reduces the bitrate to half as compared to its predecessor H.264 at the expense of huge amount of computational burden on the encoder. In the proposed work we focus on intraprediction phase of video encoding where 33 new angular modes are introduced in addition to DC and Planar mode in order to achieve high quality videos at higher resolutions. We have proposed the use of applied machine learning to HEVC intra prediction to accelerate angular mode decision process. The features used are also low complexity features with minimal computation so as to avoid any additional burden on the encoder. The Decision tree model built is simple yet efficient which is the requirement of the complexity reduction scenario. The proposed method achieves substantial average encoding time saving of 86.59%, with QP values 4,22,27,32 respectively with minimal loss of 0.033 of PSNR and 0.0023 loss in SSIM which makes it suitable for acceptance of High Efficiency Video coding in real time applications

Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.

Multi-strategy depth intra mode decision algorithm in 3D-HEVC

The new generation of 3D video coding standard, denoted as 3D High Efficiency Video Coding (3D-HEVC), uses Multi-view Video Plus Depth (MVD) format to represent stereo videos. Due to the reason that the fringe information of depth maps has great influence on the video quality of synthesized views, Depth Modeling Modes (DMMs) are introduced to maintain the quality of depth maps, while causing computational complexity increase tremendously. Therefore, a multi-strategy depth intra mode decision algorithm is proposed in this paper. It incorporates early rough mode decision (RMD) termination strategy, candidate mode reduction strategy and fast DMM decision strategy. The experimental results indicate that the proposed algorithm can obtain an average encoding time reduction of 34.38% for depth map coding, with just 0.43% BDBR increase.

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 is one of the most time-consuming operations in High Efficiency Video Coding (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 HD 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: 1) two CU classification models based on Inter $N\times N$ mode motion features and RD cost thresholds to predict the CU split decision; 2) an online training scheme for dynamic content adaptation; 3) a motion vector reuse mechanism to expedite the motion estimation process; and 4) finally introduces a computational complexity to coding efficiency tradeoff 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 Bjontegaard delta bit rate increases of 1.93%–2.26% and 2.14%–2.33% compared with 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.

Efficient Video Coding Using Visual Sensitive Information for HEVC Coding Standard

The latest high efficiency video coding (HEVC) standard introduces a large number of inter-mode block partitioning modes. The HEVC reference test model (HM) uses partially exhaustive tree-structured mode selection, which still explores a large number of prediction unit (PU) modes for a coding unit (CU). This impacts on encoding time rise which deprives a number of electronic devices having limited processing resources to use various features of HEVC. By analyzing the homogeneity, residual, and different statistical correlation among modes, many researchers speed-up the encoding process through the number of PU mode reduction. However, these approaches could not demonstrate the similar rate-distortion (RD) performance with the HM due to their dependency on existing Lagrangian cost function (LCF) within the HEVC framework. In this paper, to avoid the complete dependency on LCF in the initial phase, we exploit visual sensitive foreground motion and spatial salient metric (FMSSM) in a block. To capture its motion and saliency features, we use the dynamic background and visual saliency modeling, respectively. According to the FMSSM values, a subset of PU modes is then explored for encoding the CU. This preprocessing phase is independent from the existing LCF. As the proposed coding technique further reduces the number of PU modes using two simple criteria (i.e., motion and saliency), it outperforms the HM in terms of encoding time reduction. As it also encodes the uncovered and static background areas using the dynamic background frame as a substituted reference frame, it does not sacrifice quality. Tested results reveal that the proposed method achieves 32% average encoding time reduction of the HM without any quality loss for a wide range of videos.

Adaptive Inter CU Depth Decision for HEVC Using Optimal Selection Model and Encoding Parameters

High efficiency video coding adopts a new hierarchical coding structure, including coding unit (CU), prediction unit (PU), and transform unit to achieve higher coding efficiency than its predecessor H.264/AVC high profile. However, its hierarchical unit partitioning strategy leads to huge computational complexity. In this paper, an adaptive inter CU depth decision algorithm is proposed, which exploits both temporal correlation of CU depth and available encoding parameters. An optimal selection model of CU depth is established to estimate the range of candidate CU depth by exploiting the temporal correlation of CU depth among current CU and temporally co-located CUs. To reduce the accumulated errors in the process of CU depth prediction, the maximum depth of the co-located CUs and the coded block flag (CBF) of the current CU are used. Moreover, PU size and CBF information are also used to decide the maximum depth for the current CU. Experimental results show that the proposed CU depth decision approach reduces 56.3% and 51.5% average encoding time, and the Bjontegaard delta bit rate increases only 1.3% and 1.1% for various test sequences under the random access and the low delay B conditions, respectively.

Improved depth coding for HEVC focusing on depth edge approximation

The latest High Efficiency Video Coding (HEVC) standard has greatly improved the coding efficiency compared to its predecessor H.264. An important share of which is the adoption of hierarchical block partitioning structures and an extended number of modes. The structure of existing inter-modes is appropriate mainly to handle the rectangular and square aligned motion patterns. However, they could not be suitable for the block partitioning of depth objects having partial foreground motion with irregular edges and background. In such cases, the HEVC reference test model (HM) normally explores finer level block partitioning that requires more bits and encoding time to compensate large residuals. Since motion detection is the underlying criteria for mode selection, in this work, we use the energy concentration ratio feature of phase correlation to capture different types of motion in depth object. For better motion modeling focusing at depth edges, the proposed technique also uses an extra pattern mode comprising a group of templates with various rectangular and non-rectangular object shapes and edges. As the pattern mode could save bits by encoding only the foreground areas and beat all other inter-modes in a block once selected, the proposed technique could improve the rate-distortion performance. It could also reduce encoding time by skipping further branching using the pattern mode and selecting a subset of modes using innovative pre-processing criteria. Experimentally it could save 29% average encoding time and improve 0.10dB Bjontegaard Delta peak signal-to-noise ratio compared to the HM.

HEVC 부호화기의 적응적 참조 구조 변경 방법

본 논문에서는 HEVC (High Efficiency Video Coding) 부호화기의 성능 향상을 위한 적응적 참조 구조 변경 방법을 제안한다. 제안하는 방법은 입력 영상 내에 장면 전환, 장면 회전, Light on/off, Fade in/out 등의 이벤트 발생 시 저하되는 화면 간 예측의 성능을 향상시키기 위해 참조 픽쳐 리스트를 수정하여 참조 구조를 변경한다. GOP (Group Of Pictures) 단위로 한정된 계층적 참조 구조에서 영상 이벤트가 발생한 픽쳐를 기준으로 두 개의 서브 그룹으로 분할하고 각각의 서브 그룹 내에서 참조 픽쳐를 결정한다. 이를 통해, 시간적 중복성이 높은 참조 픽쳐를 화면 간 예측에서 사용하여 부호화 효율을 향상시킨다. 또한, HEVC의 계층적 부호화 구조 특징을 이용하여 이벤트 발생 후 처음으로 부호화되는 픽쳐를 CRA (Clean Random Access) 픽쳐로 변경하여 부호화 속도를 향상시키는 방법을 제안한다. 제안하는 방법은 이벤트 발생 후 처음으로 부호화되는 프레임은 화면 내 예측이 매우 높은 확률로 선택된다는 통계적 특징을 기반으로 화면 간 예측을 수행하지 않는다. 실험 결과 본 논문에서 제안하는 적응적 참조 구조 변경 방법은 HM 16.0 대비 CTC (Common Test Condition)에서 평균 0.3%의 BD-rate를 개선하였으며 부호화 속도는 평균 4.9% 향상시켰다. 또한, 이벤트 발생에 따른 픽쳐 타입 변경 방법은 평균 0.11%의 BD-rate 저하가 있었지만 부호화 속도를 평균 12.2% 향상시킬 수 있었다. This paper proposes adaptive reference structure decision method to improve the performance of HEVC (High Efficiency Video Coding) encoder. When an event occurs in the input sequence, such as scene change, scene rotation, fade in/out, or light on/off, the proposed algorithm changes the reference structure to improve the inter prediction performance. The proposed algorithm divides GOP (Group Of Pictures) into two sub-groups based on the picture that has such event and decides the reference pictures in the divided sub-groups. Also, this paper proposes fast encoding method which changes the picture type of first encoded picture in the GOP that has such event to CRA (Clean Random Access). With the statistical feature that intra prediction is selected by high probability for the first encoded picture in the GOP carrying such event, the proposed fast encoding method does not operate inter prediction. The experimental result shows that the proposed adaptive reference structure decision method improves the BD-rate 0.3% and reduces encoding time 4.9% on average under the CTC (Common Test Condition) for standardization. In addition, the proposed reference structure decision method with the picture type change reduces the average encoding time 12.2% with 0.11% BD-rate loss.

On lossless intra coding in HEVC with 3-tap filters

This paper presents a pixel-by-pixel spatial prediction method for lossless intra coding within High Efficiency Video Coding (HEVC). Previous pixel-by-pixel spatial prediction methods use only two neighboring pixels for prediction, based on the angular projection idea borrowed from block-based intra prediction in lossy coding, or are based on ad hoc methods applied in some intra modes. This paper explores a pixel-by-pixel prediction method which uses three neighboring pixels for prediction according to a two-dimensional correlation model, and develops a unified prediction algorithm applied in all intra modes with optimized prediction weights and neighbors. The prediction weights for each intra mode are determined from a two-stage offline optimization algorithm and a number of aspects are analyzed to determine a good trade-off between complexity and coding gain. The method is implemented in the HEVC reference software and experimental results show that it can achieve an average 11.34% bitrate reduction over the default lossless intra coding in HEVC, which is among the best reported results. The method decreases average decoding time by 12.7% while increasing average encoding time by 9.7%.