Detection of transcoded HEVC videos based on in-loop filtering and PU partitioning analyses

Xu, Qiang; Jiang, Xinghao; Sun, Tanfeng; Kot, Alex C.

doi:10.1016/j.image.2020.116109

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, filters are recognized along liberty of designing algorithm for the necessity of particular application [12][13][14][15][16]. Also, the in-loop filters works in encoder loops and decoder loops [17,18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Efficient Hybrid In-loop Filter Based on Enhanced Generative Adversarial Networks with Sample Adaptive Offset Filter for HEVC/H-265

Moji¹,

Mathivanan²

2023

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

In this manuscript, an Adaptive and Efficient Hybrid In-loop Filter based on Enhanced Generative Adversarial Network Deblocking Filter (EGANDF) with Sample Adaptive Offset filter (EGANDF-SAO-HEVC) is proposed for High Efficiency Video Coding (HEVC)/H-265. In this, the proposed hybrid in-loop filter involves EGANDF and Sample Adaptive Offset (SAO) filter that lessens the blocking artifacts caused by block-wise processing for coding unit (CU), which is mainly used for improving the video quality. Initially, EGANDF is proposed for HEVC/H-265 for removing blocking artifacts along low computation. Here, the output of EGANDF is given to the SAO filter for reducing ringing artifacts by diminishing high-frequency components during quantization. Thus, the proposed method efficiently reduces artifacts for improving video quality performance. The proposed EGANDF-SAO-HEVC method is implemented in the working platform of HEVC reference software with MATLAB. Finally, the proposed EGANDF-SAO-HEVC model has attained 27.26%, 29.65%, 12.45% higher accuracy, 33.56%, 31.8%, 28.7% higher sensitivity, 34.7%, 33.5%, 32.6% higher specificity, 46.92%, 35.7%, 41.3% lower MSE, 25.7%, 29.7%, 35.6% higher PSNR, and 25.6%, 28.9%, 13.6% higher SSIM for using basketball video sequence when compared to the existing methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive and Efficient Hybrid In-loop Filter Based on Enhanced Generative Adversarial Networks with Sample Adaptive Offset Filter for HEVC/H-265

Moji¹,

Mathivanan²

2023

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

show abstract

Color Patterns And Enhanced Texture Learning For Detecting Computer-Generated Images

Xu,

Wang

et al. 2024

The Computer Journal

View full text Add to dashboard Cite

Detection of computer-generated (CG) images can reveal the authenticity and originality of digital images. However, recent cutting-edge image generation methods make it very difficult to distinguish CG images from natural photographs. In this paper, a novel method based on color patterns and enhanced texture learning is proposed to tackle this problem. We designed and implemented the backbone network with a separation-fusion learning strategy by constructing a multi-branch neural network. The luminance and chrominance patterns in dual-color spaces (RGB and YCbCr) are leveraged to achieve a robust representation of image differences. A channel-spatial attention module and a global texture enhancement module are also integrated into a backbone network to enhance the learning of inherent traces. Experiments on several commonly used benchmark datasets and a newly constructed dataset with more realistic and diverse images demonstrate that the proposed algorithm outperforms state-of-the-art competitors by a large margin.

show abstract

Motion-Adaptive Detection of HEVC Double Compression With the Same Coding Parameters

Jiang

Sun

et al. 2022

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

Detection of transcoded HEVC videos based on in-loop filtering and PU partitioning analyses

Cited by 3 publications

References 16 publications

Adaptive and Efficient Hybrid In-loop Filter Based on Enhanced Generative Adversarial Networks with Sample Adaptive Offset Filter for HEVC/H-265

Adaptive and Efficient Hybrid In-loop Filter Based on Enhanced Generative Adversarial Networks with Sample Adaptive Offset Filter for HEVC/H-265

Color Patterns And Enhanced Texture Learning For Detecting Computer-Generated Images

Motion-Adaptive Detection of HEVC Double Compression With the Same Coding Parameters

Contact Info

Product

Resources

About