RGB-IR(RGB-Infrared) image pairs are frequently applied simultaneously in various applications like intelligent surveillance. However, as the number of modalities increases, the required data storage and transmission costs also double. Therefore, efficient RGB-IR data compression is essential. This work proposes a joint compression framework for RGB-IR image pair. Specifically, to fully utilize cross-modality prior information for accurate context probability modeling within and between modalities, we propose a Channel-wise Cross-modality Entropy Model (CCEM). Among CCEM, a Low-frequency Context Extraction Block (LCEB) and a Low-frequency Context Fusion Block (LCFB) are designed for extracting and aggregating the global low-frequency information from both modalities, which assist the model in predicting entropy parameters more accurately. Experimental results demonstrate that our approach outperforms existing RGB-IR image pair and single-modality compression methods on LLVIP and KAIST datasets. For instance, the proposed framework achieves a 23.1% bit rate saving on LLVIP dataset compared to the state-of-the-art RGB-IR image codec presented at CVPR 2022.

High-efficient image compression is a critical requirement. In several scenarios where multiple modalities of data are captured by different sensors, the auxiliary information from other modalities are not fully leveraged by existing image-only codecs, leading to suboptimal compression efficiency. In this paper, we increase image compression performance with the assistance of point cloud, which is widely adopted in the area of autonomous driving. As depicted in Figure 1 (a), we have unified the digital representation of image and point cloud, and propose the point cloud-assisted neural image codec (PCA-NIC) to enhance the preservation of image texture and structure by utilizing the high-dimensional point cloud information. As depicted in Figure 1 (b), we further introduce a multi-modal feature fusion transform module (MMFFT) to capture more representative image features, remove redundant information between channels and modalities that are not relevant to the image content.

We propose a talking face video compression framework by implicitly transforming the temporal evolution into compact feature representation. More specifically, the temporal evolution of faces, which is complex, non-linear and difficult to extrapolate, is modelled in an end-to-end inference framework based upon very compact features. This enables the high-quality rendering of the face videos, which benefits from the learning of dense motion map with compact feature representation. Therefore, the proposed framework can accommodate ultra-low bandwidth video communication and maintain the quality of the reconstructed videos. Experimental results demonstrate that compared with the state-of-the-art video coding standard Versatile Video Coding (VVC) as well as the latest generative compression scheme Face Video-to-Video Synthesis (Face_vid2vid), the proposed scheme is superior in terms of both objective and subjective quality assessment methods.

Recently, there has been a tremendous demand for high-efficiency face video communications, coinciding with the popularization of the digital human character in numerous applications. This paper demonstrates a new communication paradigm of 3D human digital characters in ultra low-bit-rate application scenarios. The paradigm is grounded on the mild assumption of the consistency and persistence of human ap-pearance, such that only the compact features that determine the pose and expression of the 3D character need to be transmitted. The proposed is also expected to benefit virtual-physical world interaction in Metaverse.

Among the recent deep image compression frameworks, transform coding together with a context-adaptive entropy model is the most representative approach to achieve the best coding performance. For entropy model, 2D mask convolution is widely utilized to capture the spatial context, which omits the correlations along channel dimension. To complement to the spatial context, a cross channel context model is proposed. For transform, if given more network layers to improve its representation ability, how to allocate these network layers in forward and inverse transform is investigated. After analyzing the scheme of deep image compression connected with loop filter, we find this investigation can be regarded as a more generalized loop filter. The proposed cross channel context model and generalized loop filter (CCCMGLF) are integrated into the deep image compression framework and jointly optimized to improve the coding performance. Experimental results demonstrate that, using PSNR as distortion metric, the proposed CCCMGLF outperforms VTM-11.0 by 1.20%, 10.82% and 5.38% in terms of BD-rate reductions for Y, U and V components, respectively, for the Kodak dataset. For the JVET CTC sequences, the proposed method outperforms VTM-11.0 by 1.44% for Y but has a coding performance loss of 24.74% and 11.91% for U and V, respectively. Over the baseline deep compression framework, the proposed method provides 7.80%, 12.66% and 11.15% performance improvement for Y, U, and V, respectively, for the Kodak dataset; 9.10%, 12.27%, and 12.68% performance improvement for Y, U and V, respectively, for the JVET CTC sequences. The proposed approaches are applicable in both image compression and intra coding in video compression.

As the latest video coding standard, Versatile Video Coding (VVC) achieves up to 40% Bjontegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video Coding (HEVC). Recently, Convolutional Neural Network (CNN) has attracted tremendous attention and shows great potential in video coding. In this paper, we design a Multi-Density Convolutional Neural Network (MDCNN) as an integrated in-loop filter to improve the quality of the reconstructed frames. The core of our approach is the multi-density block (MDB), which contains two branches: (a) the basic branch maintaining full resolution for capturing spatially-precise representations, (b) the density branch for learning rich spatial correlation with larger receptive field through down-sampling and upsampling. The feature maps of two branches will be fused into one stream repeatedly. Benefiting from this architecture, spatially-precise representations and density correlations in larger receptive field are utilized to improve the model performance and promote the model's robustness to different input resolutions. Experimental results show that, in terms of BD-rate savings for the (Y, U, V) components compared to the state-of-the-art VVC standard, the proposed MDCNN filter can achieve (5.06%, 13.86%, 13.76%) and (4.36%, 10.85%, 10.91 %) coding gain for the Random Access (RA) configuration and the All Intra (AI) configuration, respectively.

Motion compensation plays a key role in high efficiency video coding. The popular video compression standards, such as H.264/AVC and HEVC, adopt block based motion compensation technique due to its high compression efficiency and relatively low computational complexity. However, block based motion compensation may not be in accordance with the actual object boundary, potentially leading to low prediction accuracy especially in the high-texture areas. In this paper, we propose a locally refined motion compensation method to address this issue. In particular, the image segmentation is applied on the prediction block indicated by a motion vector rather than the original block to avoid explicit signaling. Furthermore, the local content is analyzed to select one segmented region and subsequently the prediction of this region is generated based on the local motion filed. Experimental results show that the proposed algorithm can achieve 0.8%, 1.1% and 1.7% bitrate savings for Random Access, Lowdelay-B and Lowdelay-P configurations respectively without introducing noticeable computational complexity.

Block partition structure has been recognized as a crucial module in video coding scheme. Recently, a quadtree plus binary tree (QTBT) block partition structure has been proposed in the Joint Video Exploration Team (JVET) development. Compared to the quadtree structure in HEVC, QTBT can achieve better coding performance with hugely increased encoding complexity. Here, we propose an effective QTBT partition decision algorithm to achieve a good trade-off between computational complexity and coding performance. In particular, at the Coding Tree Unit level, the partition parameters of QTBT are dynamically derived to adapt to the local characteristics without transmitting any overhead. Subsequently, at the Coding Unit level, a joint-classifier decision tree structure is designed to eliminate unnecessary iterations and meanwhile control the risk of false prediction. Experimental results show that the proposed algorithm can achieve 64% encoding time reduction on average with only 1.26% increase in terms of bit rate. This greatly benefits the practical implementations of QTBT in real application scenarios.

In the state-of-the-art H.265/HEVC video coding standard, the motion vector is always fixed to be 1/4-pixel resolution for the entire video sequence regardless of the different video contents, which is not efficient for prediction coding. In this paper, we propose a frame level adaptive motion vector resolution selection scheme based on a rate-distortion model in terms of motion vector resolution. In the proposed rate-distortion model, the relationship between the distortion and the motion vector resolution is approximated with a linear model. And a rate model of motion vector is built, which reflects the relationship between the coding bits of motion vector and its value. With the proposed rate-distortion model, an optimal motion vector resolution minimizing the total rate-distortion cost will be selected for each frame. Experimental results show that the proposed scheme can achieve 1.5%, 1.3% and 2.5% BD-rate gain on average for Random Access, Lowdelay-B and Lowdelay-P configurations without complexity increment.