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

Mallikarachchi, Thanuja; Fernando, Anil; Arachchi, Hemantha Kodikara

doi:10.1109/icme.2014.6890319

Cited by 28 publications

(12 citation statements)

References 8 publications

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“…However, in general, kernel-based gradient computation used in these algorithms is seen as a time-consuming operation resulting in a limited encoding time reduction. Therefore, Mallikarachchi et al in [33] propose to use local range as a texture complexity measurement when determining the CU size for given video content. However, the predetermined threshold and rigid decision trees make this algorithm less content-adaptive leading to inefficient CU size decisions for arbitrary sequences.…”

Section: Texture Properties-based Methodsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Texture Properties-based Methodsmentioning

confidence: 99%

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

et al. 2019

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“…Another approach to tackle this problem is to detect static regions in the image, which possess a similar movement, and homogenous blocks, which have a similar texture. Then it is unlikely to split them into smaller blocks [5][6][7][21][22][23][24], diminishing the number of required algorithm iterations. Hence, in order to keep the HEVC encoding flow as fast as possible, our work will exploit the homogeneous texture regions.…”

Section: Related Workmentioning

confidence: 99%

“…It is widely accepted that image blocks in which there is a spatial predominant direction should not be divided into smaller blocks in order to reduce the associated Rate Distortion (RD) cost [5][6][7]. A similar idea can be applied to areas with homogenous texture [5,[8][9][10][11] to reduce the complexity of the encoder.…”

Section: Introductionmentioning

confidence: 99%

HEVC optimizations for medical environments

et al. 2016

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HEVC/H.265 is the most interesting and cutting-edge topic in the world of digital video compression, allowing to reduce by half the required bandwidth in comparison with the previous H.264 standard. Telemedicine services and in general any medical video application can benefit from the video encoding advances. However, the HEVC is computationally expensive to implement. In this paper a method for reducing the HEVC complexity in the medical environment is proposed. The sequences that are typically processed in this context contain several homogeneous regions. Leveraging these regions, it is possible to simplify the HEVC flow while maintaining a high-level quality. In comparison with the HM16.2 standard, the encoding time is reduced up to 75%, with a negligible quality loss. Moreover, the algorithm is straightforward to implement in any hardware platform.

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“…The state-of-the-art methods for encoding complexity reduction can be generally grouped into two categories; statistical knowledge-based and learning-based approaches. For example, the algorithms proposed by Cho et al [2], and Thanuja et al [3] adopt statistical knowledge based approaches to early determine the Coding Unit (CU) size using Bayes decision rules, and texture statistics, respectively. On the other hand, learning based methods use supervised machine learning techniques to generate offline inference models utilizing a large amount of training data.…”

Section: Introductionmentioning

confidence: 99%