57.1: <i>Invited Paper</i>: Key Requirements for High Quality Picture‐Rate Conversion

Abstract-Optimizing a motion estimator (ME) for picture rate conversion is challenging. This is because there are many types of MEs and, within each type, many parameters, which makes subjective assessment of all the alternatives impractical. To solve this problem, we propose an automatic design methodology that provides 'well-performing MEs' from the multitude of options. Moreover, we prove that applying this methodology results in subjectively pleasing quality of the upconverted video, even while our objective performance metrics are necessarily suboptimal. This proof involved a user rating of 93 MEs in 3 video sequences. The 93 MEs were systematically selected from a total of 7000 ME alternatives. The proposed methodology may provide an inspiration for similar tough multi-dimensional optimization tasks with unreliable metrics.

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“…[1]- [4], [13]- [16], are applied in practice (e.g. [17], [18]), and so are alternatives based on PPC [19], [20].…”

Section: B Robustness Analysis Of Proposed Methodologymentioning

confidence: 99%

Perception-Oriented Methodology for Robust Motion Estimation Design

Heinrich

Vleuten

Haan

2014

IEEE J. Sel. Top. Signal Process.

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“…Video compression, as most widely used ME application, relies on the reduction of statistical redundancies in video content. As the luminance and color in dynamic images are most correlated in the direction of motion, any redundancy removal is highly dependent of motion estimation information [1] . On the other hand, frame rate up-conversion is consisted of using pixel-information from the existing frames for creating new, i.e.…”

Section: Introductionmentioning

confidence: 99%

Block-Matching Correlation Motion Estimation for Frame-Rate up-Conversion

KovaăźEvić

Pantić

Beric

et al. 2015

J Sign Process Syst

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This paper proposes a novel motion estimation algorithm that combines recursive block-matching and customized phase plane correlation. In comparison with alternative approaches the proposed solution shows better subjective impressions and objective measurements, such as Peak Signal to Noise Ratio and Universal Image Quality Index. Computational complexity of the algorithm is constrained and suitable for real-time implementations on platforms with limited memory and processing resources. The proof of concept is the first real-time implementation of frame-rate up-conversion on a mobile platform (Intel Merrifield), which is capable of processing 1080p video at 30 frames per second while clocked at 200 MHz.

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“…Many fast algorithms have been proposed to reduce the computation. Among them, spatio-temporal prediction methods such as 3-D Recursive Search (3DRS), are typically applied in practice [1]. Generally, 3DRS has proven to be a high quality, low-cost and true-motion estimation method for real-time systems.…”

Section: Introductionmentioning

confidence: 99%

Effective early termination using adaptive search order for frame rate up-conversion

Guo

Gao

Chen

et al. 2013

2013 IEEE International Symposium on Circuits and Systems (ISCAS2013)

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Motion Estimation (ME) palys a crucial part in frame rate up-conversion (FRUC) and video compression. One popular chip estimator, 3-D Recursive Search (3DRS), has achieved success in nowadays high definition televisions(HDTV). However, for future ultra high definition television (UHDTV) applications, the amount of computation increases exponentially. Therefore, there is an urgent need for further computational reduction. In this paper, we propose an effective early termination (ET) technique to reduce the calculation of absolute difference (AD), which is the most computation-intensive process in ME. First, motion vector (MV) candidates are examined in an adaptive search order (ASO) of their reliability to be true estimator. Then, the ET method dynamically changes the threshold for different motions to terminate accurately among reliable candidates. From the simulation, we can achieve an average computation reduction by 42% (up to 65%) with a slight PSNR degradation of 0.16dB on average.

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57.1: Invited Paper: Key Requirements for High Quality Picture‐Rate Conversion

Cited by 12 publications

References 5 publications

Perception-Oriented Methodology for Robust Motion Estimation Design

Perception-Oriented Methodology for Robust Motion Estimation Design

Block-Matching Correlation Motion Estimation for Frame-Rate up-Conversion

Effective early termination using adaptive search order for frame rate up-conversion

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