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Journal ArticleDOI

Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors

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TLDR
This paper analyzes the ME structure in HEVC and proposes a parallel framework to decouple ME for different partitions on many-core processors and achieves more than 30 and 40 times speedup for 1920 × 1080 and 2560 × 1600 video sequences, respectively.
Abstract
High Efficiency Video Coding (HEVC) provides superior coding efficiency than previous video coding standards at the cost of increasing encoding complexity. The complexity increase of motion estimation (ME) procedure is rather significant, especially when considering the complicated partitioning structure of HEVC. To fully exploit the coding efficiency brought by HEVC requires a huge amount of computations. In this paper, we analyze the ME structure in HEVC and propose a parallel framework to decouple ME for different partitions on many-core processors. Based on local parallel method (LPM), we first use the directed acyclic graph (DAG)-based order to parallelize coding tree units (CTUs) and adopt improved LPM (ILPM) within each CTU (DAGILPM), which exploits the CTU-level and prediction unit (PU)-level parallelism. Then, we find that there exist completely independent PUs (CIPUs) and partially independent PUs (PIPUs). When the degree of parallelism (DP) is smaller than the maximum DP of DAGILPM, we process the CIPUs and PIPUs, which further increases the DP. The data dependencies and coding efficiency stay the same as LPM. Experiments show that on a 64-core system, compared with serial execution, our proposed scheme achieves more than 30 and 40 times speedup for 1920 × 1080 and 2560 × 1600 video sequences, respectively.

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Citations
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Journal ArticleDOI

Spatiotemporal salient object detection by integrating with objectness

TL;DR: This paper proposes a novel spatiotemporal salient object detection method by integrating saliency and objectness, for videos with complicated motion and complex scenes, which consistently outperforms the state-of-the-art salient object Detection methods.
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Radiance map construction based on spatial and intensity correlations between LE and SE images for HDR imaging

TL;DR: A new HDR blending algorithm that uses only dual-exposure images is proposed, based on the least squares method, and includes spatial and intensity weighting functions that reduce error points and improve CRF computation accuracy.
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CFMDA: collaborative filtering-based MiRNA-disease association prediction

TL;DR: The prediction model of collaborative filtering-based miRNA-disease association prediction (CFMDA) is introduced and the model is verified by leave-one-out cross validation(LOOCV) and case validation and is faster and more effective than other state-of-the-art methods while it doesn’t need any negative samples.
Journal ArticleDOI

Exploiting textures for better action recognition in low-quality videos

TL;DR: Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.
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A smart intraocular pressure risk assessment framework using frontal eye image analysis

TL;DR: A new smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images and produces evidence of the relationship between the five extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques.
References
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Book

The Design and Analysis of Computer Algorithms

TL;DR: This text introduces the basic data structures and programming techniques often used in efficient algorithms, and covers use of lists, push-down stacks, queues, trees, and graphs.
Journal ArticleDOI

Overview of the High Efficiency Video Coding (HEVC) Standard

TL;DR: The main goal of the HEVC standardization effort is to enable significantly improved compression performance relative to existing standards-in the range of 50% bit-rate reduction for equal perceptual video quality.
Journal ArticleDOI

Scope of validity of PSNR in image/video quality assessment

TL;DR: Experimental data are presented that clearly demonstrate the scope of application of peak signal-to-noise ratio (PSNR) as a video quality metric and it is shown that as long as the video content and the codec type are not changed, PSNR is a valid quality measure.
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