Learned video codecs based on continuous latent representations struggle to operate reliably below 0.1 bits per pixel~(bpp): without a differentiable rate signal, Lagrangian optimisation cannot effectively trade reconstruction quality for bitrate at extreme compression ratios. We demonstrate that discrete latent representations sidestep this limitation entirely. In a vector-quantized~(VQ) codec, the codebook size~$K$ imposes a hard information ceiling of $\log_2 K$ bits per symbol; a learned autoregressive prior then exploits the non-uniform distribution of code usage -- which we show follows a power law -- to push actual bitrates well below this ceiling, without any rate-penalty tuning. Building on the MS-VQ-VAE architecture introduced in~\cite{kotthapalli2026msvqvae}, we sweep $K \in \{128, 256, 512, 1024\}$ under a uniform training protocol to trace four operating points on the rate-distortion~(RD) curve. We identify and resolve a critical training instability: gradient-based VQ collapses catastrophically at $K \leq 512$, whereas EMA-stabilised codebook updates with dead-code restart maintain full utilisation across all configurations. On 500 UCF101 test clips ($64\!\times\!64$, 32~frames), our models operate at 0.043-0.064~bpp -- 3.3-5$\times$ below H.264's practical floor and $5$-$7.6\times$ below H.265's floor at this resolution. Every MS-VQ-VAE configuration outperforms H.265 CRF\,36 on perceptual quality (LPIPS) despite using $5$-$7.6\times$ fewer bits. At $K{=}1024$, the model surpasses H.265 CRF\,36 on LPIPS by a margin of 0.072 absolute while using $5.1\times$ fewer bits. Codebook analysis confirms power-law index distributions and 70-85\% entropy efficiency, establishing the pipeline as a principled learned entropy coder.
Implicit Neural Representations (INRs) have recently emerged as a promising paradigm for image compression, offering a fundamentally different approach from traditional and learned codecs. Nevertheless, INR-based methods for image compression suffer from long encoding times and a consistent performance gap in classic quality metrics such as PSNR. In this work, we explore the potential of purely INR-based compression methods and we propose PaaF (Picture as a Function), a novel INR-based image codec that introduces improved architectural design, adaptive quantization, and an efficient entropy coding scheme. These components are designed to enhance rate-distortion performance while preserving the simplicity and parallelizability of INR-based decoding. Experimental results demonstrate consistent improvements over existing INR-based methods in both quantitative metrics and perceptual quality. These findings highlight the potential of INR-based approaches and contribute to narrowing the gap between functional representations and more established compression paradigms.
Siyue Teng, Ho Man Kwan, Yuxuan Jiang +2eess.IV cs.CV cs.MM
Learning-based video compression has recently achieved competitive rate-distortion performance compared to conventional video codecs. However, most existing methods rely on non-invertible analysis-synthesis transforms, with reconstruction quality subject to both quantization and transform approximation errors. This limitation becomes particularly restrictive at higher quality points, where quantization errors are small and transform-induced distortion dominates. To address this, we propose InnVC, an Invertible neural network based Video Codec for wide-range and high-fidelity compression. The core idea is to preserve an invertible main transform path prior to quantization, while injecting content-adaptive context through a compact implicit conditioning field. This decouples strongly correlated video content from harder-to-model fine details, allowing different components to specialize in complementary reconstruction tasks for more efficient compression. To further improve compressibility, we introduce a scheduled masking strategy that progressively concentrates informative content into fewer latent channels for more effective entropy coding. Experiments on the UVG and MCL-JCV benchmarks show that InnVC achieves strong compression performance over a broad quality range, being particularly effective in the high-quality regime, yielding BD-rate reductions of 21.66% in PSNR and 46.06% in MS-SSIM relative to x265 on UVG. To the best of our knowledge, InnVC is the first neural video codec covers operating poins from low bitrate to high fidelity within a single architecture scale, spanning more than 20 dB in PSNR.
Geometric partitioning has attracted increasing attention by its remarkable motion field description capability in the hybrid video coding framework. However, the existing geometric partitioning (GEO) scheme in Versatile Video Coding (VVC) causes a non-negligible burden for signaling the side information. Consequently, the coding efficiency is limited. In view of this, we propose a spatio-temporal correlation guided geometric partitioning (STGEO) scheme to efficiently describe the object information in the motion field of video coding. The proposed method can economize the bits consumed for side information signaling, including the partitioning mode and motion information. We firstly analyze the characteristics of partitioning mode decision and motion vector selection in a statistically-sound way. Based on the observed spatio-temporal correlation, we design a mode prediction and coding method to reduce the overhead for representing the above mentioned side information. The main idea is to predict the STGEO modes and motion candidates that have higher selection possibilities, which can guide the entropy coding, i.e., representing the predicted high-probability modes and motion candidates with fewer bits. In particular, the high-probability STGEO modes are predicted based on the edge information and history modes of adjacent STGEO-coded blocks. The corresponding motion information is represented by the index in a merge candidate list, which is adaptively inferred based on the off-line trained merge candidate selection probability. Simulation results show that the proposed approach achieves 0.95% and 1.98% bit-rate savings on average compared to VTM-8.0 without GEO for Random Access and Low-Delay B configurations, respectively.
Scalable compression is essential for bandwidth-adaptive transmission, yet most learned codecs are optimized for a fixed rate-distortion point, making rate adaptation costly due to re-encoding or maintaining multiple bitstreams. In this work, we propose TAFA-GSGC, a scalable learned point cloud geometry codec that enables multi-quality decoding from a single bitstream and a single trained model. TAFA-GSGC combines layered residual refinement with channel-group entropy coding, and introduces Target-Aligned Feature Aggregation module to reduce cross-layer redundancy in enhancement residuals. Our framework supports up to 9 decodable quality levels with monotonic quality improvement as more subbitstreams are received, while maintaining strong compression efficiency. Compared with the baseline PCGCv2, TAFA-GSGC attains comparable and slightly better RD performance, achieving average BD-Rate savings of -4.99% in D1 and -5.92% in D2.