Snapshot compressive imaging (SCI) and video compressive sensing (VCS) typically use fixed, globally uniform compression ratios that ignore spatio-temporal heterogeneity. We present D-STCRL, a reinforcement-learned framework that unifies adaptive sensing and semantic transmission under an explicit rate-distortion-energy objective. The pipeline comprises: (i) a Ratio Generation Network predicts per-patch ratio maps via spatio-temporal attention and 3D frequency cues; (ii) a Programmable Sensing Model emulates pixel-wise variable exposure through differentiable binary gating under a global budget; and (iii) a Frequency-Aware Swin decoder with a low-rank prior restores temporally consistent frames. A multi-objective policy gradient couples the ratio policy with reconstruction and JSCC, yielding stable training. On the NFS benchmark, D-STCRL improves PSNR by$2-3 ~\text{dB}$over fixed-ratio SCI at the same sampling budget; under 10 dB AWGN it surpasses a CRL baseline by$0.6-1.0 ~\text{dB}$while reducing transmitted symbols by up to 15 %. These results unify content-adaptive sensing and efficient transmission for next-generation cameras. Our code, configs and reproducible pipelines will be released upon acceptance.

Existing image compression models often lack personalization capabilities, treating all image regions equally and failing to meet the compression needs of different users for specific Regions of Interest (ROI). To address this challenge, we propose an innovative variable rate image compression framework that achieves user-centric dynamic compression by introducing visual in-context learning. Our method extracts cross-image semantics from user-provided visual examples to understand their intent. This semantic information is then converted into visual semantic query tokens and spatial masks to effectively guide the bit allocation of the compression model. Furthermore, we design a novel Semantic Spatial Control Block (SSCB) to fully leverage these semantic and spatial cues, thereby achieving a balance between preserving user-specified details and overall image quality. Experimental results demonstrate that our method significantly improves performance on the ROI, achieving a 31.54 % BD-Rate reduction and a 2.7479 dB BD-PSNR gain over the baseline model.

In this paper, we propose a generative face video compression framework based on affine transformations to better represent large movements without parameter transmission. It mainly consists of an encoder and decoder, and our encoder is similar to the one in [1]. Intra frame are compressed by the existing encoder, while subsequent inter frames are compressed into compact inter frame features. In the decoder, feature alignment is first established to map the decoded intra frame and inter frame features into the same domain. The aligned features are then combined with the appearance features extracted by the appearance encoder from the intra frame and fed into the coarse-fine affine transform module to establish motion estimation and compensation. The coarse affine transform focuses on global motion, while the fine affine transform deals with local motion, such as lip motion. Finally, the transformed features are fed into the image generation module to obtain the final reconstruction results.