Most frame-based learned video codecs can be interpreted as recurrent neural networks (RNNs) propagating reference information along the temporal dimension. This work revisits the limitations of the current approaches from an RNN perspective. The output-recurrence methods, which propagate decoded frames, are intuitive but impose dual constraints on the output decoded frames, leading to suboptimal rate-distortion performance. In contrast, the hidden-to-hidden connection approaches, which propagate latent features within the RNN, offer greater flexibility but require large buffer sizes. To address these issues, we propose HyTIP, a learned video coding framework that combines both mechanisms. Our hybrid buffering strategy uses explicit decoded frames and a small number of implicit latent features to achieve competitive coding performance. Experimental results show that our HyTIP outperforms the sole use of either output-recurrence or hidden-to-hidden approaches. Furthermore, it achieves comparable performance to state-of-the-art methods but with a much smaller buffer size, and outperforms VTM 17.0 (Low-delay B) in terms of PSNR-RGB and MS-SSIM-RGB. The source code of HyTIP is available at https://github.com/NYCU-MAPL/HyTIP.

This work proposes a hybrid, explicit-implicit temporal buffering scheme for conditional residual video coding. Recent conditional coding methods propagate implicit temporal information for inter-frame coding, demonstrating superior coding performance to those relying exclusively on previously decoded frames (i.e. the explicit temporal information). However, these methods require substantial memory to store a large number of implicit features. This work presents a hybrid buffering strategy. For inter-frame coding, it buffers one previously decoded frame as the explicit temporal reference and a small number of learned features as implicit temporal reference. Our hybrid buffering scheme for conditional residual coding outperforms the single use of explicit or implicit information. Moreover, it allows the total buffer size to be reduced to the equivalent of two video frames with a negligible performance drop on 2K video sequences. The ablation experiment further sheds light on how these two types of temporal references impact the coding performance.

This paper proposes a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2025. Recently, masked conditional residual coding emerged as an attractive alternative to the existing inter-frame coding frameworks, including residual coding, conditional coding, and conditional residual coding. In this work, we propose masked conditional residual B-frame coding, termed MaskCRT-B, for YUV420 videos. It features an asymmetric codec architecture that includes one joint YUV encoder and two separate Y and UV decoders. Moreover, it incorporates a bi-directional adaptive fusion module that refines the bi-directional feature maps to better tackle the prediction of the occluded and dis-occluded regions within the input video. MaskCRT-B presents a significant advancement in learned B-frame coding, outperforming the state-of-the-art conditional B-frame codec from the Grand Challenge at ISCAS 2024.

This paper aims to delve into the rate-distortion-complexity trade-offs of modern neural video coding. Recent years have witnessed much research effort being focused on exploring the full potential of neural video coding. Conditional auto encoders have emerged as the mainstream approach to efficient neural video coding. The central theme of conditional auto encoders is to leverage both spatial and temporal information for better conditional coding. However, a recent study indicates that conditional coding may suffer from information bottlenecks, potentially performing worse than traditional residual coding. To address this issue, recent conditional coding methods incorporate a large number of high-resolution features as the condition signal, leading to a considerable increase in the number of multiply-accumulate operations, memory footprint, and model size. Taking DCVC as the common code base, we investigate how the newly proposed conditional residual coding, an emerging new school of thought, and its variants may strike a better balance among rate, distortion, and complexity.

This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from a single compressed bitstream. Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the standard image reconstruction to joint decoding and denoising. Our scheme adopts a two-pronged approach. It features a latent refinement module to refine the latent representation of a noisy input image for reconstructing a noise-free image. Additionally, it incorporates an instance-specific prompt generator that adapts the decoding process to improve on the latent refinement. Experimental results show that our method achieves a similar level of denoising quality to training a separate decoder for joint decoding and denoising at the expense of only a modest increase in the decoder's model size and computational complexity.

We present a methodology for enhancing the throughput of semantic video segmentation tasks on an embedded cluster containing multiple embedded processing elements (ePEs). The methodology embraces a scalable master-slave hierarchy and features a global and local key management scheme for allocating video frames to different ePEs. The master ePE divides each video frame into frame regions, and dynamically distributes different regions to different slave ePEs. Each slave ePE executes either a segmentation path or a flow path: the former is highly accurate but slower, while the latter is faster but less accurate. A lightweight decision network is employed to determine the execution path for each slave ePE. We propose a global and local key management scheme to facilitate the execution of the embedded cluster, such that the average processing latency of each frame is significantly reduced. We evaluate the performance of our methodology on a real embedded cluster in terms of accuracy and frame rate, and validate its effectiveness and efficiency for various ePE configurations. We further provide a detailed latency analysis for different configurations of ePEs.