Panoramic images are critical for immersive VR/AR and 6DoF yet degraded by compression artifacts, projection distortion, and uneven sampling, with existing hybrid CNN-Transformer models struggling to reconcile fine details and structural consistency in panoramas; to address this, we propose Dynamic Channel Collaboration (DCC-Former) for panoramic enhancement, inspired by primate vision's hierarchical processing and three strategies: strengthening local feature representation via reparameterization and gating, enhancing global context with adaptive self-attention, and enabling cross-scale aggregation through cascaded multi-scale fusion, aligned with biological vision's ventral-dorsal stream division and fovea-periphery resource allocation to balance detail preservation, global consistency, and computational efficiency-extensive experiments on benchmark datasets demonstrate DCC-Former outperforms SOTA in restoration quality and inference efficiency, providing a practical-efficient paradigm for high-resolution panoramic enhancement.

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.

There is larger compression potential for surveillance video coding due to the inherent super-redundancy in fixed camera scenarios. So, this paper propose a novel compression framework for indoor surveillance video.

The new coding tools improved the performance for H.266/VVC but also brought challenges for hardware integer motion estimation (IME). First, the data dependency in deriving a predicted motion vector (PMV) is more severe. Second, the overhead of IME is increased by the complex partition mechanism. The challenges are tougher for IME in coding tree unit (CTU) level pipelined encoder. In this paper, we propose a hardware-friendly CTU-level IME algorithm with three innovative designs. First, a PMV prediction is proposed to derive PMVs in advance. Second, all divided blocks are categorized into either binary/quadra tree (BTQT) or ternary tree (TT) blocks. The motion vectors (MVs) of BTQT blocks are estimated with a multi-resolution search. The MVs of TT blocks are inferred from the estimated MVs with an inference algorithm. The proposed algorithm suffers $ 1.20\%$ degradation but reduced the complexity by $ 80\%$ compared to the reference software.

Recently, the learned image compression methods have achieved remarkable performance gains. However, existing learned methods lack the mechanism to capture global context for probability density model parameter estimation, or the ability of extracting features to capture spatial correlations where needs to be improved. To resolve these problems, a novel learned image compression framework is proposed in this paper.

Fractional-position motion compensation has been widely utilized in video coding standard to improve the inter prediction efficiency. In this paper, we study the three key components of the state-of-the-art method––Invertibility-driven Interpolation Filter (InvIF) and improve each of them to derive an Enhanced InvIF (EInvIF). Firstly, the deformable convolution layer is introduced to make the network's filters have the ability to change its shape and parameters to adapt to the video contents. Secondly, the generative adversarial network is utilized to increase the deep learning models' ability of approximating target distribution. Finally, the motion blur images are adopted as the regularization target instead of discrete cosine transform images. The proposed EInvIF has been integrated into HM-16.7, and the experimental results show that the proposed scheme can achieve 2.5% bitrate reduction on average.

Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding [1], such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity. To resolve this problem, this paper proposes a fast TU level rate model with higher accuracy by fully imitating the behavior pattern hid in entropy.

In video coding, inter-frame motion prediction eliminates temporal correlation greatly however bring about strong dependency characterized by inter-frame distortion propagation, which makes currently independent rate-distortion optimization (RDO) non-optimal any more. This paper proposes adaptive quantization parameter (QP) selection algorithm for global RDO by modeling the function between change of distortion propagation (ΔD) and QP change (ΔQP) as well as change of bitrate (ΔR) and ΔQP. Experimental results show that the proposed algorithm achieves promising BD-BR performance.

Prevailing image enhancement algorithms deliver flexible tradeoff at different level between image quality and implementation complexity, which is usually achieved via adjusting multiple algorithm parameters, i.e. multiple parameter optimization. Traditional exhaustive search over the whole solution space can resolve this optimization problem, however suffering from high search complexity caused by huge amount of multi-parameter combinations. To resolve this problem, an Energy Efficiency Ratio Model (EERM) based algorithm is proposed which is inspired from gradient decent in deep learning. To verify the effectiveness of the proposed algorithm, it is then applied to image de-noising algorithm framework based on non-local means (NLM) plus iteration. The experiment result shows that the optimal parameter combination decided by our proposed algorithm can achieve the comparable quality to that of the exhaustive search based method. Specifically, 86.7% complexity reduction can be achieved with only 0.05dB quality degradation with proposed method.