In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. Experimental results demonstrate that our affine motion estimation algorithm reduces encoding time by approximately 15%-35% while maintaining video bitrate and quality.

Real-time monitoring and anomaly detection of spacecraft health status in orbit are essential for ensuring mission safety and reliability. However, the strongly non-stationary nature of telemetry data under extreme space environments poses significant challenges to conventional methods that rely on the assumption of data stationarity, making it difficult to distinguish genuine anomalies from environmental fluctuations. Moreover, the inherent scarcity and non-repeatability of anomaly events lead to a severe shortage of labeled data for model training. To overcome these limitations, this paper proposes a robust unsupervised detection framework that integrates physically inspired time–frequency decomposition with deep sequential learning. The core of the framework lies in a hybrid decomposition module employing a threefold mechanism: bandwidth-adaptive local regression for nonlinear trend extraction, phase-aware periodicity decoupling via analytic signal transformation and normalized instantaneous phase computation, and residual isolation for fine-grained fluctuation modeling. The decomposed components are then processed by a multi-component attention Transformer, in which cross-attention independently enhances component-specific representations while maintaining inter-component dependencies. Finally, unsupervised anomaly detection is accomplished by combining reconstruction-based errors with correlation-aware anomaly scores. Extensive experiments conducted on three real-world spacecraft telemetry datasets and one industrial benchmark demonstrate that the proposed method achieves superior detection accuracy, stability, and robustness compared to existing state-of-the-art algorithms.