While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasant latency. In this work, we revisit the design of a diffusion-based codec and argue that multi-step sampling is not necessary for generative compression. Based on this insight, we propose OneDC, a One-step Diffusion-based generative image Codec—that integrates a latent compression module with a one-step diffusion generator. Recognizing the critical role of semantic guidance in one-step diffusion, we propose using the hyperprior as a semantic signal, overcoming the limitations of text prompts in representing complex visual content. To further enhance the semantic capability of the hyperprior, we introduce a semantic distillation mechanism that transfers knowledge from a pretrained generative tokenizer to the hyperprior codec. Additionally, we adopt a hybrid pixel- and latent-domain optimization to jointly enhance both reconstruction fidelity and perceptual realism. Extensive experiments demonstrate that OneDC achieves SOTA perceptual quality even with one-step generation, offering over 39% bitrate reduction and 20× faster decoding compared to prior multi-step diffusion-based codecs. Project: https://onedc-codec.github.io/

Recent progress in generative compression technology has significantly improved the perceptual quality of compressed data. However, these advancements primarily focus on producing high-frequency details, often overlooking the ability of generative models to capture the prior distribution of image content, thus impeding further bitrate reduction in extreme compression scenarios (< 0.05 bpp). Motivated by the capabilities of predictive language models for lossless compression, this paper introduces a novel Unified Image Generation-Compression (UIGC) paradigm, merging the processes of generation and compression. A key feature of the UIGC framework is the adoption of vector-quantized (VQ) image models for tokenization, alongside a multi-stage transformer designed to exploit spatial contextual information for modeling the prior distribution. As such, the dual-purpose framework effectively utilizes the learned prior for entropy estimation and assists in the regeneration of lost tokens. Extensive experiments demonstrate the superiority of the proposed UIGC framework over existing codecs in perceptual quality and human perception, particularly in ultra-low bitrate scenarios (≤0.03 bpp), pioneering a new direction in generative compression.

Rate control based on rate-distortion modeling is a classic problem in lossy image compression. Despite extensive research in neural image compression, its rate control remains understudied. In this paper, we introduce a variable rate neural image compression scheme that supports precise rate control with one-pass encoding. Our approach utilizes the Lagrangian multiplier method to transform rate control into an unconstrained optimization problem, mapping the target bitrate to λ for rate-distortion trade-off adjustment. We propose an improved exponential R-λ model and estimate the bitrates with a hybrid convolution-transformer network for model fitting. The encoder is controlled by λ, and a multi-layer modulation mechanism ensures variable rate ability. In our experiments, the proposed method outperforms the intra-frame coding of Versatile Video Coding (VVC). Meanwhile, the average rate control error is less than 5.1%, while maintaining almost identical rate-distortion performance and acceptable complexity.

Rate control is one of the most critical technologies of real-time video coding. It aims to make bit rate allocation and quantization parameter(QP) decisions to minimize video distortion while reducing buffering delay. However, existing solutions suffer from the problem of extra encoding delay and lack joint optimization of the region of interest(ROI) quality and buffer latency. In this paper, we propose a deep reinforcement learning(RL) based low-delay rate control method without using the information of uncoded frames to avoid the extra delay. Particularly, our RL-based rate control algorithm outputs two types of policies. The first is to make frame-level QP decisions to stabilize buffer occupancy and optimize the overall quality, while the other is in charge of adjusting the QP offset between ROI and Non-ROI regions to enhance portrait quality. Extensive experiments verify that our proposed method improves ROI and overall quality while reducing the buffer occupancy variation, compared with baseline algorithms.