Recent learned image compression (LIC) methods have demonstrated superior ratedistortion performance compared to classical image compression standards. However, their substantially higher computational complexity and inefficient entropy modeling designs raise challenges to practical deployment. To address this issue, we propose Energy-Guided Asymmetric Entropy Modeling for ultra-low-complexity learned image compression. Building on the observation that low-energy channels can be effectively modeled with less complexity, we propose a novel Asymmetric Hyperprior Transform (AHT). AHT evenly split the latent features into channel groups. Low-energy groups are processed by lightweight subnetworks, achieving reduced complexity. To further ensure proper energy allocation on channel groups, we design an Energy Allocation (EA) Loss that constrains latent features with respect to their estimated mean, thus enabling flexible control over channel energy distribution. Overall, our entropy modeling design is extremely lightweight and can be efficiently deployed on CPUs, making it well-suited for practical deployment. Experiments demonstrate that our proposed model achieves a 5% reduction in BD-rate over BPG with decoding complexity below$10 \text{kMACs} /$pixel, and achieves a BD-rate gain per decoding MACs/pixel of -645.89, striking a favorable balance between rate-distortion performance and computational cost.

Channel autoregressive entropy models are widely used in learned image compression, playing a crucial role in achieving low coding latency and state-of-the-art rate-distortion performance. Despite their effectiveness, we identify significant redundancy in vanilla context modeling that allows substantial complexity reduction without sacrificing performance. By analyzing the contributions of channel context extraction, we demonstrate that only a subset of channels is essential for entropy estimation, enabling simplified context modules. Additionally, our analysis reveals that the hyperprior, despite its large context volume, becomes less relevant for entropy estimation in later autoregressive steps. To address this, we propose Sequential Context Modeling, an approach that updates various contexts into a fixed-size context buffer, balancing context volume and reducing complexity. We validate the efficiency of our approach across multiple representative schemes, showing that it reduces up to 60% of the parameters and FLOPs in the entropy model with virtually no degradation in rate-distortion performance.

In this work, we investigate the joint optimization strategy in the scenario of video coding for machines (VCM). We formulated two kinds of joint optimization strategies, Opt_JA and Opt_JH , and compared them with the separate optimization strategy Opt_S. The three optimization strategies are illustrated in Fig. 1 . In Opt_S , we separately train the feature compression network with mean squared error (MSE). In Opt_JA , we optimize all modules jointly toward the person re-identification task. In Opt_JH , only the aggregation module and feature compression module are jointly optimized. The feature compression consists of two fully-connected (FC) layers and two batch normalization (BN) layers. Specifically, we set five compression ratios (CR): 256, 128, 64, 32, and 16.

In this paper, we first extend the video-based point cloud compression (V-PCC) to support the plenoptic point cloud compression by generating multiple attribute videos. Then based on the observation that these videos from multiple views have very high correlations, we propose encoding them using multiview high efficiency video coding. We further propose a block-based padding method that unifies the unoccupied attribute pixels from different views to reduce their bit cost.

We propose a convolutional neural network-based coefficients prediction (CNNCP) method for intra-predicted residues in the High Efficiency Video Coding (HEVC) standard. In HEVC, discrete cosine transform (DCT) or discrete sine transform (DST) is adopted to convert the intra-predicted residues in the spatial domain into coefficients in the frequency domain. Each coefficient is scalar quantized and entropy coded into the bitstream. As DCT or DST is non-optimal linear transform, there still exist linear and non-linear correlations among different coefficients after the transform. In addition, there exist coefficients' correlations between current block and neighboring blocks, as these correlations cannot be completely exploited in the intra prediction. We thus propose to perform coefficients prediction to further reduce the redundancy among coefficients. The coefficients prediction is achieved using trained convolutional neural networks (CNNs), as CNNs can build complex relationship between input and output by training with a lot of data. In addition, a flag that signals whether to perform coefficients prediction or not at the coding unit level is transmitted to decoder. The proposed CNNCP method is implemented upon the HEVC reference software. Experimental results show that the proposed method achieves on average 1.8%, 4.1%, and 4.5% BD-rate reduction ratios in Y, U, V, respectively, compared with the HEVC baseline in all-intra configuration. In particular, the average BD-rate reduction ratios for 4K test sequences are 2.9%, 6.5%, and 6.6%.

Point cloud media representation format has provided various opportunities for extended reality applications and had become widely used in volumetric content capturing scenarios. At the same time ambiguous storage format representations and network throughput are key problems for wide adoption of this media format. Compression algorithms in corresponding standard activities are aimed to solve this problem. MPEG-I standard has an aim of creating the point cloud compression methodology relying on existing video coding hardware implementations. In scope of the state-of-the-art video-based dynamic point cloud (DPC) compression method, similar 3D patches may be projected in totally different 2D positions in different frames. In this way, the motion vector predictors especially those in the patch boundary may be very inaccurate which may lead to significant bitrate increase. In this paper, we propose to use the reconstructed geometry information to help predict the motion vector more accurately and improve the coding efficiency of the attribute video. First, we propose to use the motion vector of the co-located blocks in the geometry frame as a merge candidate of the current block in the attribute frame. Second, we perform a motion estimation between the current reconstructed point cloud with only the geometry information and the reference point cloud to find the corresponding block. The motion information derived is used as motion vector predictor of the current block in the attribute frame. As far as we can see, this is the first work using the geometry information to compress the attribute in the DPC compression scenario. Significant compression efficiency is achieved with this new 3D point cloud geometry derived motion prediction scheme when compared with the state-of-the-art DPC compression method.

In this paper, based on the Hessian approximation, an incremental pruning method is proposed to compress the deep neural network. The proposed method starts from the idea of using the Hessian to measure the "importance" of each weight in a deep neural network, and it mainly has the following key contributions. First, we propose to use the second moment in Adam optimizer as a measure of the "importance" of each weight to avoid calculating the Hessian matrix. Second, an incremental method is proposed to prune the neural network step by step. The incremental method can adjust the remaining non-zero weights of the whole network after each pruning to help boost the performance of the pruned network. Last but not least, the proposed method applies an automatically-generated global threshold for all the weights among all the layers, which achieves the inter-layer bit allocation automatically. Such a method can improve performance and save the complexity of adjusting the pruning threshold layer by layer. We perform a number of experiments on MNIST and ImageNet using commonly used neural networks such as AlexNet and VGG16 to show the benefits of the proposed algorithm. The experimental results show that the proposed algorithm is able to compress the network significantly with almost no loss of accuracy, which demonstrates the effectiveness of the proposed algorithm.

Point cloud has shown great potential in many different applications including visual communication and auto-navigation systems. However, the immense size of the point cloud has been a bottleneck for its popularity in the consumer level. In this paper, we address the lossless compression of positional data of dynamic point cloud by viewing the point prediction of point cloud as Travelling Salesman Problem (TSP).

In this paper, we present a novel pseudo sequence based 2-D hierarchical reference structure for light-field image compression. In the proposed scheme, we first decompose the light-field image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then we mainly develop three technologies to optimize the 2-D coding structure. First, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically to fully exploit the correlations between different views. Second, we propose to use the distance between the current view and its reference views as the criteria for selecting better reference frames for each inter view. Third, we propose to use the spatial relative positions between different views to achieve more accurate motion vector scaling. The whole scheme is implemented in the reference software of High Efficiency Video Coding. The experimental results demonstrate that the proposed novel pseudo-sequence based 2-D hierarchical structure can achieve maximum 14.2% bit-rate savings compared with the state-of-the-art light-field image compression method.