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.

We propose an end-to-end learned scalable multilayer feature compression method. Our proposed method is illustrated in Figure 1 , where f 1 ,… ,f n stand for deep features at different layers. The deep feature f n denoting base layer is first transformed and quantized into the latent ${\hat y_n}$ . The latent ${\hat y_n}$ is then inversely transformed to reconstruct the feature as f ˆ n . In addition, the latent ${\hat y_n}$ is also fed into the entropy model of the previous-layer feature f n−1 as conditional information for the enhancement layer. The entropy model of the feature f n−1 takes both ${\hat y_{n - 1}}$ and ${\hat y_n}$ as inputs to improve compression efficiency.

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.

Rate adaption is one of the decisive factors for the applications of video compression. However, previous deep video compression methods are usually optimized for a single fixed rate-distortion (R-D) tradeoff. While they can achieve multiple bitrates by training multiple independent models, the realized bitrates are limited to several discrete points on the R-D curve and the storage cost increases proportionally to the number of models. In this paper, we propose a variable-rate scheme for deep video compression, which can achieve continuously variable rate by a single model, i.e., it can reach any point on the R-D curve. In our scheme, two deep auto-encoders are used to compress the residual and the motion vector field respectively, which directly generate the final bitstream. The basic rate adaptation can be achieved by using the R-D tradeoff parameter to deeply modulate all the internal feature maps of the auto-encoders. However, other modules in our scheme, notably motion estimation and motion compensation, also affect the final bitrate indirectly. We further use the R-D tradeoff parameter to modulate them via a conditional map, which effectively improves the compression efficiency. We use a multi-rate-distortion loss function together with a step-by-step training strategy to optimize the entire scheme. Our experiments show that the proposed scheme achieves continuously variable rate by a single model with almost the same compression efficiency as multiple fixed-rate models. The additional parameters and computation of our model are negligible when compared with a single fixed-rate model.

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%.

This paper proposes an image upsampling scheme by introducing a new gradient field sharpening transform that converts the blurry gradient field of upsampled low-resolution (LR) image to a much sharper gradient field of original high-resolution (HR) image. Different from the existing methods that need to figure out the whole gradient profile structure and locate the edge points, we derive a new approach that sharpens the gradient field adaptively only based on the pixels in a small neighborhood. To maintain image contrast, image gradient is adaptively scaled to keep the integral of gradient field stable. Finally the HR image is reconstructed by fusing the LR image with the sharpened HR gradient field. Experimental results demonstrate that the proposed algorithm can generate more accurate gradient field and produce super-resolved images with better objective and visual qualities. Another advantage is that the proposed gradient sharpening transform is very fast and suitable for low-complexity applications.

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.

Recently, the boom of media contents on the Internet raises challenges in managing them effectively and thus requires automatic media annotation techniques. Motivated by the observation that media contents are usually shared frequently in online communities and thus have a lot of social diffusion records, we propose a novel media annotating approach depending on these social diffusion records instead of metadata. The basic assumption is that the social diffusion records reflect the common interests (CI) between users, which can be analyzed for generating annotations. With this assumption, we present a novel CI-based social diffusion model and translate the automatic annotating task into the CI-based diffusion maximization (CIDM) problem. Moreover, we propose to solve the CIDM problem through two optimization tasks, corresponding to the training and test stages in supervised learning. Extensive experiments on real-world data sets show that our approach can effectively generate high quality annotations, and thus demonstrate the capability of social diffusion analysis in annotating media.

In this paper we propose an alternative decoding method for inverse wavelet transform when only partial coefficients are available. We have been inspired by the recently developed compressive sensing (CS) decoding, which is capable in recovering sparse signals from a few linear and non-adaptive measurements. Let x be a sparse signal with N entries and only K out of them are non-zero, and y be its approximation coefficients. Classic CS decoding such as l1-minimization can be applied to decode x from y, and it indeed provides better reconstruction of sparse signals than direct inverse transform, as demonstrated by our simulation results. When coefficients have been quantized, the performance of CS decoding decreases more severely compared with direct inverse transform, but still better than the latter once the signal is sparse enough.

We investigate the usage of edge-based inpainting as an intra prediction method in block-based image compression. The joint utilization of edge information and the well-known Laplace equation yields a simple and effective inpainting algorithm. As for intra prediction, the edge-based inpainting is a uniform solution, yet adaptive to local image features. During the integration of edge-based inpainting into a block-based coding scheme, edge extraction and coding are jointly considered to achieve the rate-distortion optimization. Our proposed schemes are compared with JPEG2000, and experimental results demonstrate that both PSNR gain and visible quality improvement are achieved.