DREFNet: Deep Residual Enhanced Feature GAN for VVC Compressed Video Quality Improvement

In recent years, the use of video content has experienced exponential growth. The rapid growth of video content has led to an increased reliance on various video codecs for efficient compression and transmission. However, several challenges are associated with codecs such as H.265/High Efficiency Video Coding and H.266/Versatile Video Coding (VVC) that can impact video quality and performance. One significant challenge is the trade-off between compression efficiency and visual quality. While advanced codecs can significantly reduce file sizes, they introduce artifacts such as blocking, blurring, and color distortion, particularly in high-motion scenes. Different compression tools in modern video codecs are vital for minimizing artifacts that arise during the encoding and decoding processes. While the advanced algorithms used by these modern codecs can effectively decrease file sizes and enhance compression efficiency, they frequently find it challenging to eliminate artifacts entirely. By utilizing advanced techniques such as post-processing after the initial decoding, this method can significantly improve visual clarity and restore details that may have been compromised during compression. In this paper, we introduce a Deep Residual Enhanced Feature Generative Adversarial Network as a post-processing method aimed at further improving the quality of reconstructed frames from the advanced codec VVC. By utilizing the benefits of Deep Residual Blocks and Enhanced Feature Blocks, the generator network aims to make the reconstructed frame as similar as possible to the original frame. The discriminator network, a crucial element of our proposed method, plays a vital role in guiding the generator by evaluating the authenticity of generated frames. By distinguishing between fake and original frames, the discriminator enables the generator to improve the quality of its output. This feedback mechanism ensures that the generator learns to create more realistic frames, ultimately enhancing the overall performance of the model. The proposed method shows significant gain for Random Access (RA) and All Intra (AI) configurations while improving Video Multimethod Assessment Fusion (VMAF) and Multi-Scale Structural Similarity Index Measure (MS-SSIM). Considering VMAF, our proposed method can obtain 13.05% and 11.09% Bjøntegaard Delta Rate (BD-Rate) gain for RA and AI configuration, respectively. In the case of the luma component MS-SSIM, RA and AI configurations get, respectively, 5.00% and 5.87% BD-Rate gain after employing our suggested proposed network.