Recent advances in cross-modal compression(CMC) have opened new horizons for perceptual image coding at ultra-low bitrates (below 0.1 bpp) within a generative compression paradigm, but reconstruction fidelity is often compromised, yielding visually plausible yet semantically inconsistent reconstructions. While prompt engineering with contextual optimization has been extensively explored in generative models, its potential for controlling perception-fidelity trade-offs in image compression remains largely under-explored. To address these challenges, we propose PO-CMC, a novel diffusion-based cross-modal image compression approach that introduces contextual prompt optimization to achieve efficient and perceptually faithful reconstruction. The proposed method comprises three synergistic components: an optimized image codec that produces a compact structural prior, a contextual prompt module that adaptively encodes semantic cues into compact textual embeddings, and a diffusion-based decoder that fuses the structural and semantic priors to reconstruct high-fidelity images. Extensive experiments show that PO-CMC achieves superior perceptual quality while maintaining comparable reconstruction fidelity, yielding an average BD-rate saving of 72.5 % and 79.8 % over VVC at equivalent LPIPS and DISTS levels, respectively.

This paper presents FCM-ULB, a feature coding framework designed for machine vision in extremely low-bitrate scenarios. It introduces a spatial-channel attention (SC-Attn) block to better capture global feature dependencies, enabling aggressive downsampling while preserving task-relevant information. In addition, a three-step training strategy—warm-up of vision backbone, rate-distortion optimization of feature compression, and joint fine-tuning—further improves compression and maintains task accuracy. Experiments on COCO and OpenImages show that FCM-ULB achieves much higher accuracy than existing standards like FCTMv4 at compression ratios up to$10,000 \times$, demonstrating its effectiveness for bandwidth-limited vision applications.

Neural Video Compression has emerged in recent years, with condition-based frameworks outperforming traditional codecs. However, most existing methods rely solely on the previous frame's features to predict temporal context, leading to two critical issues. First, the short reference window misses long-term dependencies and fine texture details. Second, propagating only feature-level information accumulates errors over frames, causing prediction inaccuracies and loss of subtle textures. To address these, we propose the Long-term Spatio-Temporal Enhanced Context (L-STEC) method. We first extend the reference chain with LSTM to capture long-term dependencies. We then incorporate warped spatial context from the pixel domain, fusing spatio-temporal information through a multi-receptive field network to better preserve reference details. Experimental results show that L-STEC significantly improves compression by enriching contextual information, achieving 37.01% bitrate savings in PSNR and 31.65% in MS-SSIM compared to DCVC-TCM, outperforming both VTM-17.0 and DCVC-FM and establishing new state-of-the-art performance.

Video post-processing can significantly enhance compressed video quality. Traditional methods are mostly based on handcrafted designs, such as deblocking and deringing algorithms, which rely on fixed heuristic rules and exhibit limited flexibility and adaptability. In recent years, with the rapid development of deep learning, Neural Network-based (NN-based) video post-processing methods have demonstrated remarkable coding performance. Among these, Transformer-based or multi-frame joint enhancement methods have improved reconstruction quality but still heavily depend on the prediction of known pixels, making it difficult to effectively restore high-frequency texture information lost due to the lossy compression. In contrast, diffusion models leverage their powerful generative priors and progressive denoising mechanisms to synthesize more natural and realistic high-frequency details, providing a promising solution for video post-processing. Inspired by recent advancements in conditional generative modeling, we propose a diffusion-based post-processing filter. The design incorporates a quantization parameter adaptive module and a block-based inference strategy with overlapping blocks to balance visual quality and efficiency. Experimental results demonstrate that the proposed method achieves significant improvements in both subjective and objective quality on the VTM-11.0.

This paper introduces the Spatio-Temporal Adaptive Context Optimization (STACO) method, which enhances the quality of contextual prediction across various resolutions, essential for subsequent compression. The STACO takes predicted contexts$C_t^{\{1,2,3\}}$as input and improves their quality by aligning them better with decoded features$f_{t}$, thus boosting coding efficiency. The STACO comprises Quality Perception Units and Consistency Synergy Modules, arranged in a hierarchical stacked architecture. This multi-scale design enables simultaneous processing of contexts at different spatial resolutions and facilitates information exchange through upsampling and downsampling. Enhanced contexts$\tilde{C}_{t}^{\{1,2,3\}}$are output after passing through residual connections, ensuring better alignment with reconstructed features. Using VTM-11.0 as anchor, the STACO significantly improves compression efficiency on common test condition (CTC) in HEVC, achieving an average BD-rate reduction of 17.99% for PSNR and 43.84% for MS-SSIM. By incorporating spatial quality mapping and temporal propagation, STACO offers a significant advancement in video compression.

For decades, the Bjøntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is determined using just 4–5 R-D data points, could this be sufficient? As codecs and quality metrics advance, does the conventional BD estimation still hold up? Crucially, are the performance improvements of new codecs and tools genuine, or merely artifacts of estimation flaws? We address these concerns by reevaluating BD estimation. We have established a large-scale, high-precision R-D dataset to verify the accuracy of existing BD estimation algorithms. Moreover, we propose a robust method for high-precision BD estimation across diverse compression scenarios, enhanced by a reliability assessment to determine the probability distribution of BD values from R-D sample points. This approach both assesses the reliability of BD calculations and serves as a precise BD estimator. Our method's validity is confirmed through extensive testing on a dataset we constructed. Our findings advocate for the adoption of rigorous R-D sampling and reliability metrics in future compression research to ensure the validity and reliability of results. Our code and additional experimental details are publicly accessible at https://github.com/fgvfgfg564/BDCI.

This paper propose a Image Coding for Machine (ICM) framework with Visual-Language Mimic Feature Learning (VLM-ICM). VLM-ICM decouples the position and semantic information into language modality and extracts universal features from the input image. Language, inherently more semantically compact, helps reduce the bitrate. Meanwhile, the universal features in VLM-ICM, guided by the language at the decoder side, allow for flexible domain adaptation, thereby enhancing versatility and practicality.

Neural Video Compression (NVC) has advanced significantly in recent years, with improvements in inter prediction techniques. In inter prediction, most NVC approaches utilize pixel information or temporal features from neighboring frames as reference information, while using optical flow to represent motion information. In this paper, we introduce an innovative and efficient method for Dynamic Temporal Reference Aggregation (DTRA). The proposed DTRA consists of two components: Temporal Information Compensation (TIC) and Feature Level Motion Information Enhancement (MIE). The TIC module generates compensation information by leveraging long-term temporal information from the decoding buffer, enriching the semantic content of the reference features and enhancing their texture details. The MIE module refines the motion features at the encoder side and divides the motion information into multiple groups for diverse motion alignment at the decoder side, thereby improving the motion compensation. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an average bitrate savings of 9.67% compared to state-of-the-art (SOTA) approaches.

Cloud gaming is an advanced form of Internet service that necessitates local terminals to decode within limited resources and time latency. Super-Resolution (SR) techniques are often employed on these terminals as an efficient way to reduce the required bit-rate bandwidth for cloud gaming. However, insufficient attention has been paid to SR of compressed game video content. Most SR networks amplify block artifacts and ringing effects in decoded frames while ignoring edge details of game content, leading to unsatisfactory reconstruction results. In this paper, we propose a novel lightweight network called Coding Prior-Guided Super-Resolution (CPGSR) to address the SR challenges in compressed game video content. First, we design a Compressed Domain Guided Block (CDGB) to extract features of different depths from coding priors, which are subsequently integrated with features from the U-net backbone. Then, a series of re-parameterization blocks are utilized for reconstruction. Ultimately, inspired by the quantization in video coding, we propose a partitioned focal frequency loss to effectively guide the model's focus on preserving high-frequency information. Extensive experiments demonstrate the advancement of our approach.

Rate-distortion (RD) theory is a fundamental theory for lossy image compression that treats compressing the original images to a specified bitrate with minimal signal distortion, which is an essential metric in practical application. Moreover, with the development of visual analysis applications (such as classification, detection, segmentation, etc.), the semantic distortion in compressed images are also an important dimension in the theoretical analysis of lossy image compression. In this paper, we model the rate-distortion-classification (RDC) trade-off in lossy image compression based on the previous RD model. Specifically, the classification task is used as a representative image vision analysis task to calculate the semantic distortion. For the joint optimization modeling of RDC, the optimization objective function is the code rate expressed by the mutual information $I(\cdot,\ \cdot)$ with the constraints of MSE loss $\mathrm{E}[\triangle(\cdot,\ \cdot)]$ and the classification task error rate $\varepsilon$, where $\varepsilon$ is defined by Equation (2). Define the binary classifier as: