Performance improvement of HEVC using Adaptive Quantization

Paul, Merlin; Antony, Abhilash; Sreelekha, G.

doi:10.1109/icacci.2014.6968266

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Thus, we propose a weighted DCT-based objective function to learn the parameters of the coarse-to-fine networks. The weight matrix 𝐐 is applied to the transform coefficients in the unit of 4 × 4 coefficient group (CG) to reflect the adaptive quantization, according to the positional importance of the transform coefficients [36]. The exponential weight factor is employed since the distribution of DCT coefficients follows the Laplacian distribution [37].…”

Section: Objective Functionmentioning

confidence: 99%

Coarse-to-Fine Network-Based Intra Prediction in Versatile Video Coding

Park,

Moon,

et al. 2023

Sensors

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After the development of the Versatile Video Coding (VVC) standard, research on neural network-based video coding technologies continues as a potential approach for future video coding standards. Particularly, neural network-based intra prediction is receiving attention as a solution to mitigate the limitations of traditional intra prediction performance in intricate images with limited spatial redundancy. This study presents an intra prediction method based on coarse-to-fine networks that employ both convolutional neural networks and fully connected layers to enhance VVC intra prediction performance. The coarse networks are designed to adjust the influence on prediction performance depending on the positions and conditions of reference samples. Moreover, the fine networks generate refined prediction samples by considering continuity with adjacent reference samples and facilitate prediction through upscaling at a block size unsupported by the coarse networks. The proposed networks are integrated into the VVC test model (VTM) as an additional intra prediction mode to evaluate the coding performance. The experimental results show that our coarse-to-fine network architecture provides an average gain of 1.31% Bjøntegaard delta-rate (BD-rate) saving for the luma component compared with VTM 11.0 and an average of 0.47% BD-rate saving compared with the previous related work.

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Section: Objective Functionmentioning

confidence: 99%

Coarse-to-Fine Network-Based Intra Prediction in Versatile Video Coding

Park,

Moon,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The value of the quantisation parameter can be fixed prior to encoding or adjusted continuously by the codec's rate controller (if used). Previous work has looked at ways of determining this QP value either prior to encoding [1][2][3] or in an adaptive manner during encoding [4][5][6][7] with the aim of improving the rate-distortion performance of the codec.…”

Section: Introductionmentioning

confidence: 99%