Despite significant advances in image compression, existing evaluation metrics remain poorly aligned with human visual perception-particularly under extremely low bitrates, where reconstructed images often suffer from abstract distortions or semantic degradation that are difficult for conventional metrics to capture. To address this limitation, we propose MG-VLQA, a novel multi-granularity quality assessment framework that leverages VisionLanguage Models (VLMs) to evaluate image reconstruction fidelity through the lens of semantic consistency with the original caption. Our method formulates a suite of captionderived questions spanning three complementary dimensions: (1) entity presence (semantic completeness), (2) detail fidelity (local appearance accuracy), and (3) inter-entity interactions (relational coherence). By simulating human-like perceptual judgment via VLMbased question answering and semantic similarity scoring, MG-VLQA provides a more interpretable, fine-grained, and perceptually relevant assessment of compression quality. Extensive experiments across multiple datasets and codecs demonstrate that our metric achieves higher correlation with human judgment and offers superior discriminative power.

Deep video compression has made significant progress in recent years, achieving rate-distortion performance that surpasses that of traditional video compression methods. However, rate control schemes tailored for deep video compression have not been well studied. In this paper, we propose a neural network-based$\lambda$-domain rate control scheme for deep video compression, which determines the coding parameter$\lambda$for each to-be-coded frame based on the rate-distortion-$\lambda\ (\mathrm{R}-\mathrm{D}-\lambda)$relationships directly learned from uncompressed frames, achieving high rate control accuracy efficiently without the need for pre-encoding. Moreover, this content-aware scheme is able to mitigate inter-frame quality fluctuations and adapt to abrupt changes in video content. Specifically, we introduce two neural network-based predictors to estimate the relationship between bitrate and$\lambda$, as well as the relationship between distortion and$\lambda$for each frame. Then we determine the coding parameter$\lambda$for each frame to achieve the target bitrate. Experimental results demonstrate that our approach achieves high rate control accuracy at the mini-GOP level with low time overhead and mitigates inter-frame quality fluctuations across video content of varying resolutions.

Despite the unprecedented compression efficiency achieved by deep learned image compression (LIC), existing methods usually approximate the desired bitrate by adjusting a single quality factor for a given input image, which may compromise the rate control results. Considering the Rate-Distortion ( R − D ) characteristics of different spatial content, this work introduces the block-level rate control specific for LIC.

Speech coding serves as a means of data compression, aiming to decrease the expenses related to data storage and transmission. The efficacy of compressing speech efficiently through neural networks has been demonstrated in methods using vector quantization (VQ). However, the complex procedure of VQ makes it challenging to fit into frameworks and limits compression at discrete bitrate points. This paper proposes a neural speech compression framework, which achieves flexible bitrate speech reconstruction through compact latent representation and better entropy estimation.

This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency. Specifically, we first generate multiscale attribute tensors through average pooling, by which, for any two consecutive scales, the decoded lower-scale attribute can be used to estimate the attribute probability in the current scale in one shot. Additionally, in each scale, we perform the probability estimation group-wisely following a predefined grouping pattern. In this way, both cross-scale and (same-scale) cross-group correlations are exploited jointly. Furthermore, cross-color redundancy is removed by allowing inter-color processing for YCoCg/RGB alike multi-channel attributes. The proposed method not only demonstrates state-of-the-art compression efficiency with significant performance gains over the latest G-PCC on various contents but also sustains low complexity with affordable encoding and decoding runtime.

A Transformer-based Image Compression (TIC) approach is developed which reuses the canonical variational autoencoder (VAE) architecture with paired main and hyper encoder-decoders [1], as shown in Fig. 1a. Both main and hyper encoders are comprised of a sequence of neural transformation units (NTUs) to analyse and aggregate important information for more compact representation of input image, while the decoders mirror the encoder-side operations to generate pixel-domain im-age reconstruction from the compressed bitstream. Each NTU is consist of a Swin Transformer Block (STB) [2] and a convolutional layer (Conv) to best embed both long-range and short-range information; In the meantime, a causal attention module (CAM) is devised for adaptive context modeling of latent features to utilize both hyper and autoregressive priors. The TIC rivals with state-of-the-art approaches including deep convolutional neural networks (CNNs) based learnt image coding (LIC) methods and handcrafted rules-based intra profile of recently-approved Versatile Video Coding (VVC) standard, and requires much less model parameters, e.g., up to 45% reduction to leading-performance LIC.

Recent years have witnessed the growth of point cloud based applications for both immersive media as well as 3D sensing for auto-driving, because of its realistic and fine-grained representation of 3D objects and scenes. However, it is a challenging problem to compress sparse, unstructured, and high-precision 3D points for efficient communication. In this paper, leveraging the sparsity nature of the point cloud, we propose a multiscale end-to-end learning framework that hierarchically reconstructs the 3D Point Cloud Geometry (PCG) via progressive re-sampling. The framework is developed on top of a sparse convolution based autoencoder for point cloud compression and reconstruction. For the input PCG which has only the binary occupancy attribute, our framework translates it to a down-scaled point cloud at the bottleneck layer which possesses both geometry and associated feature attributes. Then, the geometric occupancy is losslessly compressed using an octree codec and the feature attributes are lossy compressed using a learned probabilistic context model. Compared with the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based PCC (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG), our method achieves more than 40% and 70% BD-Rate (BjØntegaard Delta Rate) reduction, respectively. We would like to make all materials publicly accessible at https://njuvision.github.io/PCGCv2/ for reproducible research.

This paper introduces a 2-D index map coding of the palette mode in screen content coding extension of the High-Efficiency Video Coding (HEVC SCC) standard to further improve the compression performance. In contrast to the current 1-D search using RUN to represent the length of matched string, we bring the block width and height to describe the arbitrary rectangle shape. We also use the block vector displacement to signal the matched block distance efficiently. By enlarging the search range from current coding tree unit (CTU) to a small neighbor CTU window (i.e., 3×5 CTUs), it provides the coding efficiency comparable to the case that full-frame intra block copy is used. It is more practical to use the local search window in real life considering the trade-off between the coding efficiency and implementation cost.