Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement

Temel Kavramlar

Proposing HFUR for compressed video quality enhancement through frequency-based upsampling and iterative refinement.

Özet

The article introduces HFUR, a neural network architecture for enhancing compressed video quality by focusing on frequency-based upsampling and iterative refinement. Video compression artifacts are addressed through the proposed modules: ImpFreqUp and HIR. ImpFreqUp utilizes DCT-domain prior to reconstruct loss, while HIR refines feature maps hierarchically. Extensive experiments demonstrate the effectiveness of HFUR in achieving state-of-the-art performance.

İstatistikler

Extensive experiments show that HFUR achieves state-of-the-art performance.
The proposed method is formulated within a multi-scale framework to mitigate distortions of CUs with varying scales.
In CBR mode, videos are encoded at fixed bit rates of 200kbps and 800kbps.
Compression is conducted with QPs of 27 and 37 in CQP mode.

Alıntılar

"Video compression artifacts arise due to the quantization operation in the frequency domain."
"We propose a hierarchical frequency-based upsampling and refining neural network (HFUR) for compressed video quality enhancement."
"HFUR achieves state-of-the-art performance for both constant bit rate and constant QP modes."

Önemli Bilgiler Şuradan Elde Edildi

Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement

by Qianyu Zhang... : arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11556.pdf

Daha Derin Sorular

How does HFUR compare to traditional methods in terms of computational efficiency

Hierarchical Frequency-based Upsampling and Refining (HFUR) outperforms traditional methods in terms of computational efficiency by leveraging the implicit DCT transform in its ImpFreqUp module. By incorporating prior information from the frequency domain, HFUR can accurately reconstruct high-frequency details during cross-scale transfer, leading to more effective video quality enhancement with fewer computations. Traditional methods often rely on pixel-domain upsampling techniques that may overlook important frequency information, resulting in less efficient processing and potentially inferior results compared to HFUR.

What potential applications beyond video enhancement could the ImpFreqUp module have

The ImpFreqUp module's capability to leverage DCT-domain prior for accurate reconstruction of high-frequency information opens up potential applications beyond video enhancement. One such application could be in image restoration tasks where preserving fine details is crucial, such as medical imaging or satellite imagery analysis. By integrating the principles of ImpFreqUp into image processing pipelines, it could enhance the quality of compressed images while maintaining essential features for various domains requiring precise visual data interpretation.

How might incorporating temporal information further improve the performance of HFUR

Incorporating temporal information into HFUR could further improve its performance by enhancing motion compensation and continuity across frames. By analyzing sequential frames and considering temporal dependencies, HFUR can better predict how compression artifacts propagate over time and apply targeted enhancements accordingly. This approach would lead to more coherent video quality improvements, especially in scenarios with dynamic movement or complex scene changes where temporal consistency plays a significant role in overall visual fidelity.