Fast Mode Decision in the HEVC Video Coding Standard by Exploiting Region with Dominated Motion and Saliency Features

Podder, Pallab Kanti; Paul, Manoranjan; Murshed, Manzur

doi:10.1371/journal.pone.0150673

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…The optimized parallel structure which is designed to speed up HEVC encoders [ 14 ] includes two levels of parallelism: frame-level and task-level. A novel coding strategy is proposed in [ 15 ] to reduce the computation complexity in HEVC encoders by efficient selection of appropriate block-partitioning modes based on human visual features (HVF), and low complexity coding tree mechanism is proposed in [ 16 ] for HEVC fast CU encoding, which makes an in-depth study of the relationship among CU distribution, quantization parameter and content change. The watermark is embedded into the Quantized Transform Coefficients in [ 17 ] during the HEVC encoding process.…”

Section: Overview Of Related Work On Fast Encoding Methodsmentioning

confidence: 99%

Context-adaptive based CU processing for 3D-HEVC

Shen

2017

PLoS ONE

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The 3D High Efficiency Video Coding (3D-HEVC) standard aims to code 3D videos that usually contain multi-view texture videos and its corresponding depth information. It inherits the same quadtree prediction structure of HEVC to code both texture videos and depth maps. Each coding unit (CU) allows recursively splitting into four equal sub-CUs. At each CU depth level, it enables 10 types of inter modes and 35 types of intra modes in inter frames. Furthermore, the inter-view prediction tools are applied to each view in the test model of 3D-HEVC (HTM), which uses variable size disparity-compensated prediction to exploit inter-view correlation within neighbor views. It also exploits redundancies between a texture video and its associated depth using inter-component coding tools. These achieve the highest coding efficiency to code 3D videos but require a very high computational complexity. In this paper, we propose a context-adaptive based fast CU processing algorithm to jointly optimize the most complex components of HTM including CU depth level decision, mode decision, motion estimation (ME) and disparity estimation (DE) processes. It is based on the hypothesis that the optimal CU depth level, prediction mode and motion vector of a CU are correlated with those from spatiotemporal, inter-view and inter-component neighboring CUs. We analyze the video content based on coding information from neighboring CUs and early predict each CU into one of five categories i.e., DE-omitted CU, ME-DE-omitted CU, SPLIT CU, Non-SPLIT CU and normal CU, and then each type of CU adaptively adopts different processing strategies. Experimental results show that the proposed algorithm saves 70% encoder runtime on average with only a 0.1% BD-rate increase on coded views and 0.8% BD-rate increase on synthesized views. Our algorithm outperforms the state-of-the-art algorithms in terms of coding time saving or with better RD performance.

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Section: Overview Of Related Work On Fast Encoding Methodsmentioning

confidence: 99%

Context-adaptive based CU processing for 3D-HEVC

Shen

2017

PLoS ONE

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“…This work ensures time saving without degradation in the quality. Podder [21] has also proposed an interesting software method to reduce the ME time. Based on human visual features (HVF), an efficient decision of the appropriate block partitioning mode has been obtained.…”

Section: Related Workmentioning

confidence: 99%

“…A time saving without degradation in the quality is obtained in this work. Another interesting method was presented by Podder et al [21], where human visual features (HVF) are used for the selection of appropriate block partitioning modes. This work offered 41.44% reduction in terms of time for the standard class video sequences (SCVS).…”

Section: Criteria Description Formulamentioning

confidence: 99%

Fast Motion Estimation’s Configuration Using Diamond Pattern and ECU, CFM, and ESD Modes for Reducing HEVC Computational Complexity

Khemiri¹,

Bahri²,

Belghith³

et al. 2020

Digital Imaging

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The high performance of the high efficiency video coding (HEVC) video standard makes it more suitable for high-definition resolutions. Nevertheless, this encoding performance is coupled with a tremendous encoding complexity compared to the earlier H264 video codec. The HEVC complexity is mainly a return to the motion estimation (ME) module that represents the important part of encoding time which makes several researches turn around the optimization of this module. Some works are interested in hardware solutions exploiting the parallel processing of FPGA, GPU, or other multicore architectures, and other works are focused on software optimizations by inducing fast mode decision algorithms. In this context, this article proposes a fast HEVC encoder configuration to speed up the encoding process. The fast configuration uses different options such as the early skip detection (ESD), the early CU termination (ECU), and the coded block flag (CBF) fast method (CFM) modes. Regarding the algorithm of ME, the diamond search (DS) is used in the encoding process through several video resolutions. A time saving around 46.75% is obtained with an acceptable distortion in terms of video quality and bitrate compared to the reference test model HM.16.2. Our contribution is compared to other works for better evaluation.

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“…, and FourPeople (1280×720-class E) (detail to be found in [25] [26]). To avoid the biasness with color or contrast, initially we design experiment using the gray scale components only.…”

Section: A Data Capture and Pre-processingmentioning

confidence: 99%

A Novel Quality Metric Using Spatiotemporal Correlational Data of Human Eye Maneuver

Podder¹,

Paul²,

Murshed

2017

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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The popularly used subjective estimator-mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a noreference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants' eyetracker recorded gaze-data indicate more concentrated eyetraversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS.

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Fast Mode Decision in the HEVC Video Coding Standard by Exploiting Region with Dominated Motion and Saliency Features

Cited by 10 publications

References 23 publications

Context-adaptive based CU processing for 3D-HEVC

Context-adaptive based CU processing for 3D-HEVC

Fast Motion Estimation’s Configuration Using Diamond Pattern and ECU, CFM, and ESD Modes for Reducing HEVC Computational Complexity

A Novel Quality Metric Using Spatiotemporal Correlational Data of Human Eye Maneuver

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