The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard.

Visual tracking is a key research area in computer vision, as tracking technology is increasingly being applied in daily life, it has high-research significance. Visual tracking technology usually faces various challenging interference factors, among which, a similar background is one of the factors that has a greater impact on the tracking process. Kernelized Correlation Filter (KCF) tracking algorithm can track targets quickly by using circulant matrix, and has good tracking effect, so it is widely used in the tracking field. However, when the target is interfered by similar objects, the filter template in KCF cannot effectively distinguish between the target and the interfering object. This is because the filter only uses the texture gradient feature as the description object of the target, which will make the KCF algorithm extremely sensitive to the change of the target; therefore, the filter has difficultly making a judgment in the unstable scene, cannot accurately describe the target state, and finally leads to tracking failure. Therefore, this paper fuses Color Names (CN) on the basis of the original Histogram of Oriented Gradients (HOG) feature of KCF, which can obtain a more comprehensive feature representation, and realize the application of combined features to improve the anti-interference ability of KCF in complex scenes. In addition, this paper also uses the peak response of correlation filtering as the judgment condition to determine whether the current tracking result is stable. When the filter is in an unstable tracking state, the proposed algorithm will select the value with high confidence from its multiple responses as the candidate target of the Siamese network, and the deep learning network is used as the incremental learning method of the filter. The Channel Attention is introduced into the network layer, so that the network can adaptively reason and adjust the extracted universal features, and the enhanced feature information is used as the final discriminant basis. Finally, according to the response, the target with the smallest error compared with the target template is selected from multiple candidate targets as the final tracking result. The experimental results show that the average accuracy and average success rate of the proposed algorithm are significantly improved compared with the classical tracking algorithm, especially in dealing with similar target interference.

Product quantization (PQ) is a widely adopted technique for efficient approximate nearest neighbor (ANN) search in high-dimensional spaces, offering a favorable balance between accuracy and memory efficiency. However, standard PQ suffers from high online computational cost when the number of subspaces is high. To address this dilemma, we propose Pyramid Product Quantization (PPQ), a novel adaptive quantization framework that dynamically selects the most suitable number of subspaces for different segments of each data vector. This leads to a significant reduction in the number of addition operations required during approximate distance computation, significantly accelerating online search. Experimental results demonstrate that the proposed PPQ method effectively lowers the computational complexity of product quantization and its variants, without compromising retrieval accuracy.

The utilization of Artificial Intelligence-Generated Content (AIGC) has attracted widespread attention in video content creation. To generate high-quality videos, this paper presents a controllable multimodal fusion architecture for AIGC-driven short-video production. This architecture employs hierarchical constraint mechanisms and a multimodal attention fusion mechanism to enhance video content coherence and user controllability. Specifically, a scene coherence scheme is first designed to construct graph-based global and transition-level constraints by integrating text descriptions, reference images, and audio features. By leveraging the extracted style vector data, preliminary video clips are then generated through a combination of the cross-modal fusion unit and the spatio-temporal consistency unit. Finally, a fine-grained adjustment mechanism is implemented to ensure logical consistency and stylistic uniformity in the AIGC-generated videos. Experimental results indicate that the proposed architecture improves generation quality, controllability, and cross-segment coherence under the adopted evaluation settings.