Inter prediction is a fundamental component of modern video coding, where the quality of reference frames critically affects motion compensation accuracy and overall compression efficiency. However, relying solely on reconstructed low-temporal-layer frames imposes significant limitations, as these frames often suffer from compression artifacts that degrade prediction quality. To overcome this limitation, we propose a Local-Global spatiotemporal context modeling-based virtual reference frame generation network (LGCM-Net) that synthesizes high-quality reference frames based on reconstructed frames, as shown in Fig. 1. The proposed network integrates hierarchical feature extraction with long-range dependency modeling, where QP-conditioned modulation is applied to shallow features to adapt them to quantization-induced quality variations, enabling temporally and structurally consistent reference generation closely aligned with the to-be-coded frame. Moreover, a coarse-to-fine multi-stage optical flow refinement mechanism is employed to progressively enhance motion accuracy, and a residual refiner further compensates remaining motion estimation errors and reconstruction artifacts to deliver a more accurate final prediction. The proposed method achieves$5.37 \%, 9.96 \%$, and 9.91% BD-rate reduction under the Random Access configuration in VVC reference Software (VTM-11.0_nnvc-10.0 w/o NN Coding tools) for the$\mathrm{Y}, \mathrm{U}$, and V components, respectively.

In this paper, we propose a novel implicit neural representation (INR)-based image compression paradigm that generate image-specific latents from a multi-scale Gaussian noise tensor which is deterministically generated using a shared random seed. As shown in Fig. 1, inspired by the normalization strategy in E2E codecs where latents are transformed into Gaussian noise to demonstrate the removal of spatial redundancy, we explore the inverse direction: generating Gaussian latents directly from Gaussian noise. Specifically, given a noise input$z$, we introduce a Gaussian Parameter Prediction (GPP) module that estimates the Gaussian distribution parameters, enabling one-shot latent generation via the reparameterization trick. The predicted latent is then transformed into the reconstructed image through a synthesis network.

Recently, implicit neural representation (INR)-based image compression has attracted growing attention due to its low decoding complexity. However, most existing methods require multiple encoding processes to generate multiple bitstreams and reconstructions at different quality levels, leading to high storage and computational overhead. A more practical solution is progressive compression, which supports multiple qualities within a single bitstream. In this paper, we propose PINIC (Progressive Implicit Neural Image Compression), a DWT-guided framework that integrates progressive functionality into INR-based compression for the first time. Specifically, DWT energy is applied to adaptively allocate the energy of latent channels and latent will be entropy coded in the order of channel energy. Moreover, we adopt reparameterization to decouple network structures between training and inference, retaining the expressive power of complex architectures while reducing parameter count at deployment. Extensive experiments demonstrate that PINIC achieves state-of-the-art rate-distortion performance on the Kodak and CLIC2020P datasets, while simultaneously supporting progressive compression.

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.

The subblock merge mode, in which the current coding block is split into multiple subblocks for motion compensation but still inherits the motion at the coding block level, improves the accuracy of the inter prediction and reduces the signaling overhead of motion information at the same time. And thus, it was adopted into versatile video coding (VVC) due to its high efficiency and continually improved in the enhanced compression model (ECM). However, the motion used in subblock merge mode was inherited from the previously coded blocks and may not match well with the current coding block. Thus, to improve the accuracy of the motion for the subblock merge mode, it is proposed to apply template matching (TM) based motion refinement. For subblock temporal motion vector predictor (SbTMVP) candidates, it is proposed to refine both the motion displacement and subblock motion vectors (MVs) based on TM; for affine motion candidates, it is proposed to refine the affine model, including base MV and non-translation parameters, based on TM. The proposed method was implemented on top of ECM, and the experiment results show that by applying the proposed method, it achieves {−0.23%(Y), −0.23%(U), −0.20%(V)} and {−0.09%(Y), −0.36%(U), −0.01%(V)} BD-rate reduction on random access (RA) and low delay B (LDB) configurations, respectively. Due to the good trade-off between performance and complexity, the proposed method was adopted into ECM.

Screen content has emerged as a prominent medium in our increasingly connected world. However, compressed screen content images often suffer from unpleasant artifacts, significantly obstructing the comprehension of text and graphic regions. In this paper, we introduce a quality enhancement framework specifically designed for compressed screen content images. We first propose a dataset for enhancing the quality of screen content images affected by various levels of compression distortion, using state-of-the-art Versatile Video Coding with screen content coding techniques enabled. Given the unique characteristics of screen content images, our enhancement framework incorporates B-spline representation to mitigate the quality degradation caused by compression. Additionally, we focus on recovering distorted text by detecting text regions within the degraded image and generating a pristine textual map to guide the recovery process. Experimental results demonstrate that our proposed method effectively enhances the quality of reconstructed screen content images across different compression distortion levels, leading to the quantitative and qualitative improvement.

Recent exploration efforts in JVET (Joint Video Experts Team of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC29) has achieved progresses on neural network-based video coding (NNVC)11NNVC is also the name of the reference software for evaluating neural network-based video coding technologies in JVET. The project locates at https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM. Latest version of NNVC features two normative deep tools, i.e., neural network-based intra prediction and neural network-based in-loop filtering. Specifically, the neural network-based filtering in NNVC supports three operating points, known as VLOP (very low-complexity operating point), LOP (low-complexity operating point), and HOP (high-complexity operating point). LOP filter receives more attention among these three due to its favorable performance-complexity trade-off. In this paper, we introduce CCLOP, a cross-component enhanced LOP filter. CCLOP builds upon LOP filter in NNVC but incorporates deep luma features for chroma filtering. We conduct extensive experiments to verify the effectiveness of CCLOP. Compared with NNVC-10, the latest reference software of NNVC, CCLOP achieves {-0.13%, −2.27%, −3.11%}, {-0.18%, −2.07%, −3.21%}, and {-0.03%, −1.81%, −2.51%} BD-rate changes on average for {Y, Cb, Cr} under random-access, low-delay, and all-intra configurations respectively, while maintaining the same complexity as existing LOP filter ([email protected] kMAC/pixel, [email protected] kMAC/pixel).

Neural compression methods often rely on highly expressive models to fit large datasets, resulting in significant decoding complexity. Overfitted codecs have been proposed as an alternative to reduce decoding complexity. However, these approaches typically lack flexibility in encoding configurations. To address this, we introduce CD, a neural video compression method that employs picture-wise overfitting. CD is built upon the Cool-chic video framework [1], but incorporates Decoupled representations for motion and residue. Additionally, we propose an effective training strategy for CD to further enhance its performance.

As an incubator of next generation video coding techniques beyond versatile video coding (VVC) capability, enhanced compression model (ECM) has been initiated by the Joint Video Exploration Team (JVET). This paper presents an Inter CCP merge mode to improve the coding performance for chroma inter coding. Experimental results show that Inter CCP merge mode provides an average Bjontegaard delta rate (BD-rate) change of 0.01%/-0.69%/-0.76% and -0.03%/-2.31%/-2.38% on Y/Cb/Cr components, respectively, compared with ECM-10.0 in RA/LDB configurations under the common test condition, with a negligible running time change.

In this paper, we present a Conv-Attention, a decoder-friendly attention mechanism, in an effort to advancing the practical application of the artificial intelligence-based image coding. More specifically, the proposed method is tailored for JPEG AI, which is the latest advanced neural-network based image coding standard. By identifying the obstacles by profiling the decoding complexity of JPEG AI, the attention module accounts for a significant proportion, which mainly attributes to the intricate network structure and involvement of less efficient operations. Conv-Attention model is composed with plain convolution and activation computations, equipping with sub-scaling and up-scaling design, such that the non-adjacent features can be well captured, leading to the reduction of decoding complexity and maintenance of the synthesis and attentive capability. Simulation results verify the effectiveness of the proposed method with JPEG AI reference software, wherein the decoding complexity is reduced by 80% with negligible coding performance loss. The proposed method was adopted in the 100th JPEG meeting.

Geometric partitioning mode (GPM) splits a coding block into two partitions, which can be non-rectangular, separated by a straight splitting line. Two uni inter-predictions generated by translational motion compensation (TMC) for the two GPM partitions are blended to obtain the final prediction. With a promising coding gain, GPM has been adopted in versatile video coding (VVC). Beyond VVC, enhanced compression model (ECM) introduces several extensions on GPM, but GPM still cannot deal with affine motions well. This paper presents a method of GPM with affine prediction (GPM-affine). A GPM partition can be predicted by affine motion compensation (AMC) or TMC, indicated by a flag. A GPM partition predicted by AMC can be blended with the other GPM partition predicted by AMC, TMC, or intra-prediction. Experimental results show that GPM-affine provides an average luma BD-rate saving of 0.19% compared to ECM-10.0 in random access configurations under the common test condition, with a negligible running time change. On sequences with rich affine motions, 1% coding gain in average is observed. Currently, GPM-affine is under study in exploration experiments (EE) for ECM in JVET.

In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) standard. We have augmented the latest ECM reference software with well-designed coding techniques, including block partitioning, deep learning-based loop filter, and the activation of block importance mapping (BIM) which was integrated but previously inactive within ECM, further enhancing coding performance. We evaluate the coding performance of the proposed framework with extensive experiments on the JVET dataset compared with ECM10.0 and VTM-11.0. Due to the testing environment and the coding complexity of the ECM, we did not conduct testing on Class A. The QPs are set as 22, 27, 32, 37, and 42. Compared with ECM-10.0, our method achieves 6.26%, 13.33%, and 12.33% BD-rate savings for the Y, U, and V components under random access (RA) configuration. The traditional hybrid coding framework combined with the three coding tools can further improve compression efficiency and has great potential for performance improvement.

The essence of dynamic point cloud compression lies in the effective modeling of temporal context information, which poses significant challenges owing to the unstructured and sparse characteristics of point clouds. Existing dynamic compression methods exhibit a limited capacity to capture and leverage inter-frame information. Consequently, in this paper, we propose a Dynamic Point Cloud Compression framework with Spatio-Temporal Transformer-style Modeling (DPCC-STTM) to compress point cloud sequences within a latent space. To effectively extract and fully utilize temporal context, we introduce a spatio-temporal transformer-style modeling module, which performs effective modeling of the rich temporal information based on the correlation of temporal content. Furthermore, we introduce a multi-scale temporal processing module that captures temporal correlations across short and long ranges of multi-frame point clouds. This module also fuses modeled temporal information to enhance the prediction accuracy of potential features for the current frame. Extensive experiments demonstrate the superiority of our proposed framework, validated through both objective evaluation and subjective perception.

Resampling-based coding, i.e. down-sampling before encoding and up-sampling after decoding, has been recognized to be an effective tool for compressing high-resolution videos at low bitrates. The newest video coding standard, Versatile Video Coding (VVC), supports resampling-based coding via a mechanism named Reference Picture Resampling (RPR), where the spatial resolution can be changed without inserting an intra frame. Intuitively, it is not wise to utilize a single resolution throughout the whole video, because frames with different contents may prefer different coding resolutions. In this paper, we propose a smart reference picture resampling approach, namely smart-RPR, where the coding-resolution of a frame is determined based on the property of the frame without multiple-pass encoding. Specifically, we first down- and up-sample a frame without considering compression and compare the up-sampled frame with the original frame to obtain the resampling distortion, which is then compared with a threshold to decide whether to code the frame in a resampling way. Then, we build up an exponential model to approximate the optimal threshold. In addition, we also study how to derive the coding parameters of the down-sampled frame to achieve better performance. Simulation results on the VTM-12.0 show that the proposed method could achieve 2.72%, 5.29%, and 10.82% BD-rate reductions for Y, Cb, and Cr components, respectively, with lower encoding and decoding complexity.

Existing deep learning based approaches for coding artifact removal mainly focus on the relationship between reconstruction and original signals. Although the CNN-based in-loop filtering reveals significant potential, the filtering procedure is separated from the rate distortion optimization (RDO) in the block-based hybrid framework. In this paper, we propose a joint rate distortion optimization scheme [1] with CNN-based in-loop filter for the Versatile Video Coding (VVC), with the goal of excavating the potential of in-loop filtering. In particular, a joint rate distortion optimization is presented, to consider the CNN-based in-loop filtering in the coding unit. More specifically, the CNN-based in-loop filter is involved in the partitioning mode selection, and meanwhile the prediction mode determination process remains unchanged. The proposed joint optimization is only applied to the CUs, whose height and width are no larger than 64. To keep the encoding complexity as low as possible, a light-weight version of CNN-based model is provided to facilitate the encoding optimization. As shown in Table 1, the proposed method brings 9.61% and 12.81% BD-Rate gains for luma component under all intra and random access configurations, respectively, on VTM-11.0-nnvc platform.

Cross-component prediction, which takes advantage of inter-channel correlations, predicts the chroma block with the luma reconstructed block according to associated linear model. Instead of involving all available reference samples in building the linear model, in this paper, we propose a sub-sampled approach that utilizes at most four neighboring chroma samples and their corresponding down-sampled luma samples, leading to significantly reduced operations in the derivation of model parameters at both encoder and decoder. The proposed scheme is hardware friendly in terms of the overheads of memory access and clock cycles, and greatly benefits the practical implementations of the emerging video coding standard in real applications. Extensive experiments reveal that the proposed sub-sampled method provides simple operations and robust coding performance, leading to the adoption by Versatile Video Coding (VVC) Standard and the third generation Audio Video Coding Standard (AVS3).

The quad-tree plus binary-tree (QTBT) coding unit (CU) partitioning structure, which has been adopted to the next generation video coding standard, shows promising coding performance when compared with the conventional quad-tree structure in HEVC. In this paper, we propose the Extended Quad-tree (EQT) partitioning, which further extends the QTBT scheme and increases the partitioning exibility. More specifcally, EQT splits a parent CU into four sub-CUs of dierent sizes, which can adequately model the local image content that cannot be elaborately characterized with QTBT. Meanwhile, EQT partitioning allows the interleaving with BT partitioning for enhanced adaptability. Experimental results on the JEM7-QTBT-Only platform show that EQT brings better coding performance with 3.17%, 3.20% and 3.06% BD-Rate gains under random access, low-delay P and low-delay B configurations, respectively.

In this paper, History-based Motion Vector Prediction (HMVP) is presented for video coding. With the proposed method, a table of HMVP candidates is maintained and updated on-the-fly. After decoding one inter-coded block, the table is updated by appending the associated motion information to the table as a new HMVP candidate. A First-In-First-Out (FIFO) rule is applied to manage the table. The HMVP candidates could be added to the Advanced Motion Vector Prediction (AMVP) candidate list as additional motion vector predictors. And they could also be added to the merge candidate list as additional merge candidates. With the proposed method, the motion information of previously coded blocks even not adjacent to the current block can be utilized for more efficient motion vector prediction. Simulation results have validated the efficiency of HMVP, wherein up to 4% BD rate saving could be achieved. The proposed method has been adopted by the next generation video coding standard, named Versatile Video Coding (VVC) developed by Joint Video Exploration Team (JVET).