With the exponential growth of video traffic and the continuous evolution of video coding standards, video transcoding has become essential for existing bitstreams to benefit from the advanced features of new video compression technologies. Typically, video transcoding involves decoding an existing bitstream and re-encoding the decoded sequence into a target format. A key challenge in transcoding is the inevitable presence of compression artifacts in the decoded sequences, which, if not properly addressed, can degrade transcoding efficiency by causing suboptimal bit allocation and disrupting core coding processes. In this article, a learned video transcoding framework (LVT) is proposed to optimize video transcoding, leveraging coding priors from the input bitstream to guide the transcoding process. In the framework, to mitigate the adverse effects of compression artifacts, a Coding Priors-Guided Spatial Feature Transform module is designed, which utilizes coding prior features to adaptively modulate intermediate features through spatial affine transformations, enhancing bit allocation and suppressing artifacts. Additionally, a Coding Priors-Guided Quality Adapter module is proposed to generate a compression degradation representation using coding priors, which dynamically interacts with intermediate features to enable the network to perceive and adapt to different levels of degradation in the input video. Furthermore, a Motion Vectors-Guided Flow Refinement module is proposed to reduce prediction errors caused by artifacts. It refines optical flow predictions by using motion vectors from the bitstream as auxiliary information. Extensive experiments demonstrate that our framework outperforms both existing traditional and learned video codecs in transcoding performance, achieving an average bitrate saving of 20.3% compared to the H.266/VVC reference software VTM under the practical YUV420 setting measured with PSNR.

This paper proposes a lightweight image super-resolution (SR) pre-processing method for JPEG compression. Our method reduces computational costs for super-resolution and ensures consistent image quality by avoiding unknown super-resolution algorithms on the user side, which may cause inconsistencies in the quality of the delivered images. Specifically, the proposed method contains an SR model with only 2.8K parameters, followed by the JPEG encoder. For the lightweight SR model, a Separable Structural Reparameterization (SSR), which consists of a Cross-Scale Directional Reparam (CDR) module that improves spatial learning and a Channel Reparam Integration (CRI) module that enhances feature interaction, is proposed to improve the capability of the model for information enhancement. Furthermore, we introduce a JPEG compression-aware training strategy to make the super-resolution process aware of the subsequent JPEG compression. This strategy uses a differentiable JPEG codec for gradient backpropagation during training and switches to the standard JPEG codec for deployment. Extensive experiments have been conducted to validate the effectiveness of our method.

Learned image compression methods have shown remarkable performance and expansion potential compared to traditional codecs. Currently, there are two mainstream image compression frameworks: one uses stacked convolution and other uses window-based self-attention for transform coding, most of which aggregate valuable dependencies in a fixed spatial range. In this paper, we focus on extending content-adaptive aggregation capability and propose a dynamic kernel-based transform coding. The proposed adaptive aggregation generates kernel offsets to capture valuable information with dynamic sampling convolution to help transform. With the adaptive aggregation strategy and the sharing weights mechanism, our method can achieve promising transform capability with acceptable model complexity. Besides, considering the coarse hyper prior, the channel-wise, and the spatial context, we formulate a generalized entropy model. Based on it, we introduce dynamic kernel in hyper-prior to generate more expressive side information context. Furthermore, we propose an asymmetric sparse entropy model according to the investigation of the spatial and variance characteristics of the grouped latents. The proposed entropy model can facilitate entropy coding to reduce statistical redundancy while maintaining inference efficiency. Experimental results demonstrate that our method achieves superior rate–distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.

In this paper, we extend our prior research named DKIC [1] and propose the perceptual-oriented learned image compression method, PO-DKIC, which is shown in Figure 1 . Specifically, DKIC adopts a dynamic kernel-based dynamic residual block group to enhance the transform coding and an asymmetric space-channel context entropy model to facilitate the estimation of Gaussian parameters. Based on DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to enhance visual quality. Furthermore, to maximize the overall perceptual quality under a rate constraint, we formulate this challenge into a constrained programming problem and use the Linear Integer Programming method for resolution. The experiments demonstrate that our proposed method can generate realistic images with richer textures and finer details when compared to state-of-the-art image compression techniques.

Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.

In recent years, learned video compression methods have improved substantially. However, most existing algorithms focus on exploring short-term temporal information, thus constraining the compression capability. In this paper, to further boost video compression performance, we exploit both long-and short-range temporal information and consider bidirectional temporal information. For long- and short-range temporal information exploration, we adopt temporal prior and progressive guided motion compensation. Specifically, with the continuously updating strategy, the temporal prior can provide rich mutual information between the overall prior and the current frame, facilitating Gaussian parameter prediction in the entropy model. Besides, the progressive guided motion compensation utilizes flow-to-kernel and scale-by-scale stable guiding strategy, thus achieving robust and effective inter coding. Furthermore, existing low-latency-oriented methods often suffer from strong error propagation, so we extend the framework with a bidirectional prediction scheme and propose the bidirectional temporal prior. Extensive experimental results demonstrate that our method can obtain competitive performance compared to the state-of-the-art learned video compression approaches and the standard reference software HM-16.22.

Learned image compression (LIC) has shown significant potential and better rate-distortion performance than traditional techniques. However, existing CNN-based approaches or window-based self-attention methods can only capture spatial information within fixed ranges. To tackle this limitation, we propose a novel method with dynamic spatial aggregation for transform coding. Our approach introduces enhanced adaptive aggregation, which generates kernel offsets to capture relevant information within content-dependent ranges, improving the transform process. Furthermore, we define a generalized coarse-to-fine entropy model that considers global context, channel-wise information, and spatial context in a coarse-to-fine manner. Additionally, our method takes into full consideration the model’s efficiency and complexity. By introducing the asymmetric entropy model structure and efficient heterogeneous convolution, our approach maintains lower coding complexity and higher decoding speed while ensuring performance. Experimental results demonstrate a substantial improvement in rate-distortion performance achieved by our method when compared to traditional compression methods like VTM and BPG, as well as some LIC methods.