Free-Viewpoint Video (FVV) enables immersive 3D experiences, but efficient compression of dynamic 3D representation remains a major challenge. Existing dynamic 3D Gaussian Splatting methods couple reconstruction with optimization-dependent compression and customized motion formats, limiting generalization and standardization. To address this, we propose D-FCGS, a novel Feedforward Compression framework for Dynamic Gaussian Splatting. Key innovations include: (1) a standardized Group-of-Frames (GoF) structure with I-P coding, leveraging sparse control points to extract inter-frame motion tensors; (2) a dual prior-aware entropy model that fuses hyperprior and spatial-temporal priors for accurate rate estimation; (3) a control-point-guided motion compensation mechanism and refinement network to enhance view-consistent fidelity. Trained on Gaussian frames derived from multi-view videos, D-FCGS generalizes across diverse scenes in a zero-shot fashion. Experiments show that it matches the rate-distortion performance of optimization-based methods, achieving over 40 times compression compared to the baseline while preserving visual quality across viewpoints. This work advances feedforward compression of dynamic 3DGS, facilitating scalable FVV transmission and storage for immersive applications.

Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been largely overlooked. To address this issue, our work focuses on a low-complexity design while maintaining compression performance. We introduce a novel Learned Lossless Low-complexity Text Compression method (L3TC). Specifically, we conduct extensive experiments demonstrating that RWKV models achieve the fastest decoding speed with a moderate compression ratio, making it the most suitable backbone for our method. Second, we propose an outlier-aware tokenizer that uses a limited vocabulary to cover frequent tokens while allowing outliers to bypass the prediction and encoding. Third, we propose a novel high-rank reparameterization strategy that enhances the learning capability during training without increasing complexity during inference. Experimental results validate that our method achieves 48% bit saving compared to gzip compressor. Besides, L3TC offers compression performance comparable to other learned compressors, with a 50x reduction in model parameters. More importantly, L3TC is the fastest among all learned compressors, providing real-time decoding speeds up to megabytes per second.

Efficiently compressing HD/UHD content has long been challenging due to high bitrate costs. Instance-adaptive enhancement methods try to tackle this issue by compressing a video at reduced resolution and enhancing it using a neural model specifically overfitted for this video. However, existing methods focus solely on spatial super-resolution (SR) and under-utilize the videos' temporal redundancy. Their limited management of the model's updated parameters also causes excessive overfitting overheads. Therefore, this paper introduces IASTE, the first instance-adaptive enhancement method based on spatial-temporal enhancement (STE), and incorporates low-rank adaptation (LoRA) for efficient model overfitting. Specifically, we downscale videos spatially and temporally to reduce the data volume and achieve efficient video compression. Then, we overfit a specific STE model for each video and use it to enhance the decoded video's spatiotemporal resolution. Leveraging the video swin transformer's strong capability in capturing spatiotemporal correlations, we design a lightweight and efficient model to implement video STE. The model is overfitted for each video using LoRA. By freezing the pre-trained model and selectively updating a few low-rank matrices, the bitrate overhead for model storage can be mitigated. Experiments prove that compared to directly compressing high-frame-rate (HFR), high-resolution (HR) videos, our method achieves around 30% BD-Rate gains on the CTC and UVG datasets, about 15% gains on the YoutubeUGC dataset, and about 10% gains on the ultra-long videos in the Xiph dataset.

HTTP adaptive streaming (HAS) constructs bitrate ladders to deliver videos with the best possible quality under varying network conditions. Though per-shot content adaptive encoding (CAE) largely improves the compression efficiency by constructing the optimal bitrate ladder for each video shot, it suffers from excessive encoding complexity as all the points in the operating space (typically resolution × bitrate) need to be encoded and compared. To address this issue, this paper proposes an efficient bitrate ladder construction method that encodes only a subset of operating points, then uses curve fitting and inter-curve prediction to estimate other points’ RD performance. The proposed method enables low-complexity ladder construction even for high-dimension operating spaces that incorporate dimensions like encoding presets. Experiments show that this method can achieve RD performance comparable to the original per-shot CAE with only 42% encoding points. Even when minimizing the encoding points to 3.6% of the original CAE, it achieves 15% BD-Rate improvements compared to using the fixed bitrate ladder.

Text patterns typically exhibit distinct boundaries and sparse color histograms. However, in current hybrid codec frameworks, the positions of coding units are often misaligned with the text patterns, resulting in prediction and color mapping tools consuming a large number of bits to indicate these patterns. Nowadays, some text detection and recognition methods have been proposed to accurately locate and analyze the text regions in screen images. Combined with these techniques, we propose a character position-aware compression framework for screen text image. On the encoder side, a low-complexity detection method is adopted to locate the text characters. Then it copies the detected characters to the position aligned with the coding unit (CU) grid to form a text layer. This text-layer representation can further increase the efficiency of existing screen content coding tools such as Intra Block Copy (IBC). Moreover, we design several compression tools based on this representation. We extend the two Motion Vector (MV) prediction modes: Adaptive Motion Vector Prediction (AMVP) and Merge. We modify the MV encoding syntax according to the layout characteristics of the text layer. We present a Gradient-guided In-loop Filter (GIF) to sharpen the text lines using a convolutional network. Experiments conducted on VVC reference software VTM all_intra configuration show that the proposed framework can achieve an average bitrate savings of 4.6% and 3.6% under the w/ GIF and w/o GIF versions, with a corresponding increase in CPU encoding complexity of 72% and 10%.

For the latest video coding standard Versatile Video Coding (VVC), the encoding complexity is much higher than previous video coding standards to achieve a better coding efficiency, especially for intra coding. The complexity becomes a major barrier of its deployment and use. Even with many fast encoding algorithms, it is still practically important to control the encoding complexity to a given level. Inspired by rate control algorithms, we propose a scheme to precisely control the intra encoding complexity of VVC. In the proposed scheme, a Time-PlanarCost (viz. Time-Cost, or T-C) model is utilized for CTU encoding time estimation. By combining a set of predefined parameters and the T-C model, CTU-level complexity can be roughly controlled. Then to achieve a precise picture-level complexity control, a framework is constructed including uneven complexity pre-allocation, preset selection and feedback. Experimental results show that, for the challenging intra coding scenario, the complexity error quickly converges to under 3.21%, while keeping a reasonable time saving and rate-distortion (RD) performance. This proves the efficiency of the proposed methods.