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.

The latest generation of standard versatile video coding (VVC) continues to utilize hybrid coding architecture to further promote compression performance, where the intra-mode decision module selects the optimal mode to balance bitrate and coding distortion. With regard to chroma intra modes, a scheme that uses a cross-component linear model (CCLM) is involved by utilizing the component correlation between luma and chroma, which could implicitly introduce distortion propagation from luma blocks to subsequent chroma prediction blocks during coding, impacting the result of a Lagrange optimization. This paper presents an improved intra-mode decision-based modified Lagrange multiplier for chroma components in VVC. The characteristics of chroma intra prediction are examined in depth, and the process of an intra-mode decision is analyzed in detail; then, the coding distortion dependency between the luma and chroma is described and incorporated into a Lagrange optimization framework to determine the optimal mode. The proposed method achieves an average bitrate-saving effect of 1.23% compared with the original scheme by using a dependent rate-distortion optimization in an All-Intra configuration.

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.