This report presents the VCIP 2025 Grand Challenge on Ultra Low-Bitrate Video Compression, which aims to promote research progress in perceptually optimized and computationally efficient video compression under extreme bandwidth constraints. The challenge focuses on scenarios such as emergency communication, remote monitoring, and low-power transmission, where conventional codecs like HEVC and AV1 struggle to maintain acceptable perceptual quality. Two benchmark tracks are introduced to assess the trade-off between compression ratio, visual quality, and complexity: Track 1 (50 kbps) targets extreme low-bitrate conditions, while Track 2 (200 kbps) allows moderately higher bitrates under real-time constraints. Participants were required to satisfy strict limits on encoding and decoding complexity, evaluated in kMac per pixel and per-frame runtime. The evaluation combined both objective metrics (PSNR, SSIM, VMAF, LPIPS) and subjective human perception scoring to comprehensively assess quality and efficiency. The results reveal two complementary trends for the future of video compression: (1) efficiency-oriented codec architecture design enabling low-latency deployment on edge devices, and (2) perceptual enhancement through post-decoding restoration guided by temporal and semantic priors. Together, these directions signal a paradigm shift from traditional rate–distortion optimization toward a broader rate–perception–complexity trade-off. The insights gained from this challenge are expected to inspire future standards and generative compression models for perceptually-driven, adaptive, and bandwidth-efficient video communication.

The learning-based video compression method has made significant progress in recent years, exhibiting promising compression performance compared with traditional video codecs. However, prior works have primarily focused on advanced compression architectures while neglecting the rate control technique. Rate control can precisely control the coding bitrate with optimal compression performance, which is a critical technique in practical deployment. To address this issue, we present a fully neural network-based rate control system for learned video compression methods. Our system accurately encodes videos at a given bitrate while enhancing the rate-distortion performance. Specifically, we first design a rate allocation model to assign optimal bitrates to each frame based on their varying spatial and temporal characteristics. Then, we propose a deep learning-based rate implementation network to perform the rate-parameter mapping, precisely predicting coding parameters for a given rate. Our proposed rate control system can be easily integrated into existing learning-based video compression methods. The extensive experimental results show that the proposed method achieves accurate rate control on several baseline methods while also improving overall rate-distortion performance.

The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transforms providing more compact bit represen-tations, and achieve faster coding speed on parallel devices over their classical counterparts. Those properties evoked the attention of both scientific and industrial communities, resulting in the standardization activity JPEG-AI. The verification model for the standardization process of JPEG-AI is already in development and has surpassed the advanced VVC intra codec. To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed. However, the current state of the JPEG-AI verification model experiences significant slowdowns during bit rate matching, resulting in suboptimal performance due to an unsuitable model. The proposed methodology offers a gradual algorithmic optimization for matching bit rates, resulting in a fourfold acceleration and over 1% improvement in BD-rate at the base operation point. At the high operation point, the acceleration increases up to sixfold.

With the growing demand for video applications, many advanced learned video compression methods have been developed, outperforming traditional methods in terms of objective quality metrics such as PSNR. Existing methods primarily focus on objective quality but tend to overlook perceptual quality. Directly incorporating perceptual loss into a learned video compression framework is non-trivial and raises several perceptual quality issues that need to be addressed. In this paper, we investigated these issues in learned video compression and propose a novel High Visual-Fidelity Learned Video Compression framework (HVFVC). Specifically, we design a novel confidence-based feature reconstruction method to address the issue of poor reconstruction in newly-emerged regions, which significantly improves the visual quality of the reconstruction. Furthermore, we present a periodic compensation loss to mitigate the checkerboard artifacts related to deconvolution operation and optimization. Extensive experiments have shown that the proposed HVFVC achieves excellent perceptual quality, outperforming the latest VVC standard with only 50% required bitrate.

With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention. Recently, learned image compression methods had exceeded their classical counterparts in terms of rate-distortion performance. However, continuous rate adaptation remains an open question. Some learned image compression methods use multiple networks for multiple rates, while others use one single model at the expense of computational complexity increase and performance degradation. In this paper, we propose a continuously rate adjustable learned image compression framework, Asymmetric Gained Variational Autoencoder (AG-VAE). AG-VAE utilizes a pair of gain units to achieve discrete rate adaptation in one single model with a negligible additional computation. Then, by using exponential interpolation, continuous rate adaptation is achieved without compromising performance. Besides, we propose the asymmetric Gaussian entropy model for more accurate entropy estimation. Exhaustive experiments show that our method achieves comparable quantitative performance with SOTA learned image compression methods and better qualitative performance than classical image codecs. In the ablation study, we confirm the usefulness and superiority of gain units and the asymmetric Gaussian entropy model.

Sparse representation based optimization has emerged as a new paradigm for solving classification problems and has achieved satisfactory performances. Recent research, however, has revealed its noteworthy limitation in handling samples with high intra-class pair-wise correlations. In this paper, we study this problem from a novel perspective of de-correlating the input data. A new method is proposed by combining Principle Component Analysis (PCA) and the Group Lasso method. The highly correlated training samples are first orthogonalized using PCA, and then the Group Lasso algorithm is adopted for performing the classification. Experimental results show that our proposed method over-performs the Group Lasso method in the face recognition application on two public databases.