The rapid progress of multimedia technology has led to an increased focus on enhancing the quality of experience (QoE) for video. Specifically, the demand for low-latency and high-quality decoding has grown significantly. Compressed Video Quality Enhancement (CVQE) methods based on Deep Neural Networks (DNNs) have achieved remarkable success. However, most of the methods suffer from high computational complexity, thereby limiting their practicality in low-latency scenarios. Recently, Look-Up Table (LUT) methods have shown great efficiency, which makes them considerably promising in the field of low-latency CVQE. In this paper, we propose an efficient multi-frame deformable Look-Up Table structure for CVQE. Firstly, we design an efficient CNN to explore the inter-frame correlation and then predict the multi-scale convolution offsets. Secondly, we introduce a temporal feature extraction module and a multi-scale fusion module. We first exploit the predicted offsets to guide sampling for precise temporal alignment and extract multi-frame information. Then, higher quality frames are reconstructed from the fused multi-scale features. During the inference, we convert these two modules into LUTs to achieve a sound trade-off between model performance and computational complexity. Experiments demonstrate that our proposed method dramatically outperforms the state-of-the-art LUT-based methods, and obtains competitive performance compared to CNN-based methods with the capability to run in real-time(30fps) at 1080p resolution.

In this paper, we propose a neural representation for videos that enables real-time quality-scalable decoding, called QS-NeRV. QS-NeRV comprises a Self-Learning Distribution Mapping Network (SDMN) and Extensible Enhancement Networks (EENs). Firstly, SDMN functions as the base layer (BL) for scalable video coding, focusing on encoding videos of lower quality. Within SDMN, we employ a methodology that minimizes the bitstream overhead to achieve efficient information exchange between the encoder and decoder instead of direct transmission. Specifically, we utilize an invertible network to map the multi-scale information obtained from the encoder to a specific distribution. Subsequently, during the decoding process, this information is recovered from a randomly sampled latent variable to assist the decoder in achieving improved reconstruction performance. Secondly, EENs serve as the enhancement layers (ELs) and are trained in an overfitting manner to obtain robust restoration capability. By integrating the fixed BL bitstream with the parameters of EEN as an extension pack, the decoder can produce higher-quality enhanced videos. Furthermore, the scalability of the method allows for adjusting the number of combined packs to accommodate diverse quality requirements. Experimental results demonstrate our proposed QS-NeRV outperforms the state-of-the-art real-time decoding INR-based methods on various datasets for video compression and interpolation tasks.