Continuous Detail-Exposure Control via Latent Skipping for View-Level Privacy in Learned Image Compression

Learned image compression typically optimizes rate–distortion performance, but offers limited control over how much visual detail is exposed at decoding time. We study controllable detail exposure: suppressing fine-grained texture while preserving a structurally interpretable view, thereby limiting what downstream tasks can extract from the decoded view, a guarantee we refer to as view-level privacy. We propose sCBH (s-Contract with Blockhead), a channel-level skipping framework trained without any downstream-task objective, in which a single scalar $s$ (the detail-exposure strength) determines how many latent channels are retained in the bitstream. At $s=1$, sCBH is bit-exact to the base codec, adding zero quality tax; decreasing $s$ progressively removes texture channels from the bitstream and reduces bitrate. A lightweight structural decoder reconstructs a low-detail representation from the structural channels, preserving coarse scene structure under aggressive skipping. Skipping and stuffing strategies are adapted to the causal order of each entropy model. Experiments on ELIC, Hyperprior, CCM, and HPCM show continuous control over bitrate and detail; across four downstream tasks, performance degrades according to each task's reliance on texture versus structure. The source code is publicly available on https://anonymous.4open.science/r/sCBH.