Directional Variance: A measure to find the directionality in a given image segment

Jayachandra, Dakala; Makur, Anamitra

doi:10.1109/iscas.2010.5537403

Cited by 19 publications

(18 citation statements)

References 12 publications

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“…The directional variance metric described in [38] uses the traditional definition of ''digital line'' for the discrete space …”

Section: Sub-sampling Of Integer Latticementioning

confidence: 99%

“…Cumulative variance along digital lines is proposed in [38], which is proven as an efficient metric for texture orientation detection in images with large size. However, due to the constraints imposed by factors such as the PU sizes, the high density of the intra-prediction modes in HEVC, and the need to reduce the encoder's computational burden, the following modifications and novel features are proposed:…”

Section: Computation Of Mean Directional Variance (Mdv) Along Co-linesmentioning

confidence: 99%

“…Finally, instead of calculating the cumulative variance of the digital lines as is proposed in [38], the MDV is (13).…”

Section: Computation Of Mean Directional Variance (Mdv) Along Co-linesmentioning

confidence: 99%

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A unified architecture for fast HEVC intra-prediction coding

Ruiz

Fernández–Escribano

Martínez

et al. 2017

J Real-Time Image Proc

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The high efficiency video coding (HEVC) is the new video coding standard, which obtains over 50% bit rate savings compared with H.264/AVC for the same perceptual quality. Intra-prediction coding in HEVC achieves high coding performance in expense of high computational complexity, due to the exhaustive evaluation of all available coding units (CU) sizes, with up to 35 prediction modes for each CU, selecting the one with the lower rate distortion cost, among other new features. This paper presents a Unified Architecture to form a novel fast HEVC intra-prediction coding algorithm, denoted as fast partitioning and mode decision. This approach combines a fast partitioning decision algorithm, based on decision trees, which are trained using machine learning techniques, and a fast mode decision algorithm, based on a novel texture orientation detection algorithm, which computes the mean directional variance along a set of co-lines with rational slopes using a sliding window over the prediction unit. Both algorithms proposed apply a similar approach, exploiting the strong correlation between several image features and the optimal CTU partitioning and the optimal prediction mode. The key point of the combined approach is that both algorithms compute the image features with low complexity, and the partition decision and the mode decision can also be taken with low complexity, using decision trees (if-else statements) and by selecting the minimum directional variance between a reduced set of directions. This approach can be implemented using any combination of nodes, obtaining a wide range of time savings, from 44 to 67%, and light penalties from 1.1 to 4.6%. Comparisons with similar state-of-the-art works show the proposed approach achieves the best trade-off between complexity reduction and rate distortion.

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“…The directional variance metric described in [38] uses the traditional definition of ''digital line'' for the discrete space …”

Section: Sub-sampling Of Integer Latticementioning

confidence: 99%

Section: Computation Of Mean Directional Variance (Mdv) Along Co-linesmentioning

confidence: 99%

See 1 more Smart Citation

A unified architecture for fast HEVC intra-prediction coding

Ruiz

Fernández–Escribano

Martínez

et al. 2017

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…The MDV is a modified version of the metric proposed by Jayachandra and Makur in [29], which calculates the cumulative variance along digital lines. The directional variance metric has proved to be an accurate technique to detect the content orientation with low complexity while achieving a high accuracy in the framework of the directional discrete wavelet transform and Directionlets.…”

Section: Introductionmentioning

confidence: 99%

“…Instead using the digital lines proposed of [29], our algorithm estimates the dominant gradient for each PU computing the variance along the pixels digital co-lines using rational slopes defined in discrete lattice [30]. The MDV is computed over sliding window achieving high accuracy texture orientation detection and a high correlation with the predictors' selection in HEVC.…”

Section: Introductionmentioning

confidence: 99%