Template-generated videos (TGVs), created by applying animation templates to static images, have become increasingly prevalent, producing massive user-generated content with highly consistent motion patterns. However, existing video compression schemes are designed to eliminate motion redundancy within individual videos, while overlooking the shared motion patterns widespread across TGVs. To address this limitation, we propose a novel compression scheme that effectively leverages inter-video motion priors to enhance the compression efficiency of TGVs. Specifically, the proposed scheme operates as a two-stage pipeline. In the first stage, high-quality motion priors are identified from a representative TGV based on spatial texture and prediction error. In the second stage, these motion priors are intelligently integrated to expand the motion representation space beyond the local candidate lists in Merge and AMVP modes, thereby enabling the codec to remove inter-video redundancy. Experimental results on the versatile video coding test model (VTM-23.0) demonstrate consistent coding gains across various compression scenarios for TGVs, achieving average BD-rate savings of$1.07 \%, 1.38 {\%}$, and 1.18% under low-delay P (LDP), low-delay B (LDB), and random access (RA) configurations, respectively.

Learned reference picture resampling control (LRPRC) adaptively adjusts the coding scale for each frame using an offline-trained neural network. It demonstrates promising promising rate-distortion (R-D) performance improvements over traditional methods, particularly in high-resolution, low-bit-rate video coding scenarios. However, existing LRPRC methods rely exclusively on locally optimal decision labels derived from greedy strategies for network training, leading to suboptimal control performance. To address this limitation, we introduce a novel data-centric solution that substantially improves training label quality, thereby enhancing overall LRPRC performance. Specifically, our key contribution is a parallelized beam search-based coding scale labeling algorithm, which captures decision dependencies across coding steps and produces higher-quality training labels with enhanced R-D performance. By fully exploiting the intra-trellis and inter-trellis parallelism of beam search and hierarchical coding, our proposed labeling algorithm achieves logarithmic-squared time complexity, making it highly suitable for large-scale cluster computing. We validate this simple yet effective data-centric LRPRC approach in the Versatile Video Encoder (VVenC) using 4K video sequences. Experimental results demonstrate that merely upgrading the beam search labels (without any neural architecture re-designs) consistently outperforms the state-of-the-art LRPRC method, achieving BD-rate reductions of 5.09%, 3.98%, and 3.59% under thefast,medium, andslowpresets, respectively.

The popularity of template-generated videos has recently experienced a significant increase on social media platforms. In general, videos from the same template share similar temporal characteristics, which are unfortunately ignored in the current compression schemes. In view of this, we aim to examine how such temporal priors from templates can be effectively utilized during the compression process for template-generated videos. First, a comprehensive statistical analysis is conducted, revealing that the coding decisions, including the merge, non-affine, and motion information, across template-generated videos are strongly correlated. Subsequently, leveraging such correlations as prior knowledge, a simple yet effective prior-driven compression scheme for template-generated videos is proposed. In particular, a mode decision pruning algorithm is devised to dynamically skip unnecessarily advanced motion vector prediction (AMVP) or affine AMVP decisions. Moreover, an improved AMVP motion estimation algorithm is applied to further accelerate reference frame selection and the motion estimation process. Experimental results on the versatile video coding (VVC) platform VTM-23.0 demonstrate that the proposed scheme achieves moderate time reductions of 14.31% and 14.99% under the Low-Delay P (LDP) and Low-Delay B (LDB) configurations, respectively, while maintaining negligible increases in Bjøntegaard Delta Rate (BD-Rate) of 0.15% and 0.18%, respectively.

This paper proposes a highly efficient sample adaptive offset (SAO) mode decision algorithm. By leveraging both the directional correlations between the SAO and intra-prediction decisions, and the SAO decisions' spatial correlations, the SAO mode candidates are effectively pruned during the rate-distortion optimization process, accelerating the SAO encoding process with negligible BD-rate loss.

Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints. However, optimizing the neural network can be challenging due to its nature of learning quantized latent representations. In this paper, our key finding is that minimizing the latent entropy is, to some extent, equivalent to maximizing the conditional source entropy, an insight that is deeply rooted in information-theoretic equalities. Building on this insight, we propose a novel structural regularization method for the neural image compression task by incorporating the negative conditional source entropy into the training objective, such that both the optimization efficacy and the model's generalization ability can be promoted. The proposed information-theoretic regularizer is interpretable, plug-and-play, and imposes no inference overheads. Extensive experiments demonstrate its superiority in regularizing the models and further squeezing bits from the latent representation across various compression structures and unseen domains.

Cooperative adaptive cruise control (CACC) systems face critical cybersecurity challenges due to vulnerabilities in vehicle-to-vehicle (V2V) networks and onboard sensors. Covert attacks have become a critical research focus in CACC systems due to their inherent risks and stealthiness. In this article, a novel attack model called covert coordinated attack (CCA) is proposed, and its detection and isolation schemes are proposed to enhance the resilience of CACC systems against such an attack. First, the specific CCA strategies are proposed by revealing dual-channel attack coordination mechanisms targeting both sensors and V2V networks. Second, the detection framework based on unknown input observers (UIOs) is designed by monitoring the residual-based health characteristic. Third, based on the established two auxiliary systems, an attack isolation strategy integrating UIOs and extended state observers (ESOs) is proposed for attack reconstruction and system performance recovery. Finally, numerical simulations are conducted to validate both the stealthiness of CCA and the effectiveness of the proposed attack detection and isolation methods.

Recent advancements in learned rate-distortion optimization (RDO) showcase that by making the intra coding decisions based on a learned measure, the encoding can be significantly accelerated without incurring much coding loss. Despite great progress in complexity reduction, the dependency issue has been largely neglected in the current learned RDO research. In this study, aiming to tap the full potential of dependent learned RDO, we first derive a probabilistic RDO framework for theoretical analysis, under which the classic and the learned RDO problems are equivalent to the maximum a posteriori (MAP) inference and the distribution imitation, respectively. Subsequently, we probabilistically revisit dependency considerations in the intra RDO research. Our key finding is that the existing learned RDO scheme can only produce a measure that indicates the local “goodness” of coding decisions. We therefore further discuss the opportunities for learning a dependent measure that is more optimal in the long run. Finally, as learning an accurate measure for the full decision space could be extremely challenging, taking the High Efficiency Video Coding (HEVC) intra coding as a case study, we experimentally identify that the prediction decision accounts for the majority of the dependent optimization gain and is of the utmost value to be learned, paving the way for future research on dependent learned RDO.

Compressing high-resolution videos under low bitrate constraints is a challenging task. Resampling-based compression, which reduces the resolution before encoding and restores it after decoding, has great potential to improve the rate-distortion performance in such scenarios. In this paper, we propose a learning-based frame-level coding scale control scheme that enhances the coding performance by adjusting the coding scale for each frame. The scheme cooperates with the Reference Picture Resampling of the latest video coding standard Versatile Video Coding (VVC), which allows coding scale variations on each frame. More specifically, a dataset with 5200 videos is created by a greedy rate-distortion optimization algorithm employed to select the optimal coding scale for each frame. A neural network-based decision model is further incorporated into VVC, learning to predict the coding scale for each frame in one pass. The scheme is implemented into the Fraunhofer Versatile Video Encoder (VVenC), a fast and efficient VVC encoder, and evaluated on 4 K contents. Experimental results show that the proposed scheme outperforms GOP-based coding scale adaptation methods, achieving average bitrate savings of 3.06% and 4.14% in terms of PSNR and MS-SSIM.

We extend the beam-search based joint rate-distortion optimization (BSJRDO) [1] to High Efficiency Video Coding (HEVC) and investigate the impact of different decisions on it. In BSJRDO, unlike the greedy search, which only maintains one locally optimal path at each decision stage, multiple paths are kept as candidates for future referencing.

The intra block copy (IBC) mode introduced can significantly improve the coding efficiency for screen content. However, with the mixed content, the coding performance improvement of IBC is limited and while also introducing the increases of the coding complexity. This paper proposes an early determination scheme for IBC prediction to reduce the encoding complexity over mixed content sequences containing captured content regions. Our method is based on a refined CTU-level screen content detection method, which can better distinguish screen content in mixed scenes. Experimental results show that the proposed method reduces encoding complexity with moderate coding performance loss on mixed content sequences.

This article studies the impact of constrained optimal stealthy attacks on the state estimator, where man-in-the-middle attacks with a linear form can compromise innovations transmitted through a wireless network. First, a novel resource-constrained attack model is proposed, in which there are only a finite number of attack instants within a fixed interval. Second, the evolution of the estimation error covariance under attacks is obtained, and the covariance at the ultimate instant of the attack interval is regarded as the attacker’s cost function. Moreover, a relaxed condition of the strict stealthiness, named Kullback–Leibler divergence, is employed to describe the attacker’s the stealthiness metric. Third, the one-time and holistic optimization problems of stealthy attacks are solved by exploiting the Lagrange multiplier method. Then the constrained optimal attack strategies are obtained to produce the largest ultimate estimation error covariance. Finally, two simulation cases are provided to confirm the correctness of the designed attack strategies.

In this paper, we present the first attempt at determining where the achievable rate-distortion (R-D) performance bound in versatile video coding (VVC) intra coding is when considering the mutual dependency in the rate-distortion optimization (RDO) process. In particular, the abundant search space of encoding parameters in VVC intra coding is practically explored with a beam search-based joint rate-distortion optimization (BSJRDO) scheme. As such, the partitioning, prediction and transform decisions are jointly optimized across different coding units (CUs) with a customized search subset instead of the full space. To make the beam search process implementation-friendly for VVC, the dependencies among the CUs are truncated at different depths. To facilitate finer computational scalability, the beam size is flexibly adjusted based on the characteristics of the CUs, such that the operational points that satisfy different complexity demands for diverse applications can be practically obtained. The proposed BSJRDO approach, which fully conforms to the VVC decoding syntax, can serve as both the way toward the optimal RDO bound and a practical performance-boosting solution. BSJRDO is further implemented on a VVC coding platform (VVC Test model (VTM) 12.0), and extensive experiments show that BSJRDO can achieve 1.30% and 3.22% bit rate savings compared to the VTM anchor under the common test condition and low-bit-rate coding scenarios, respectively. Moreover, the performance gain can also be flexibly customized with different computational overheads.