LOONA: Linearized Objectives over Noise Atoms for Controllable Zero-Shot Diffusion Image Compression

Zero-shot diffusion-based codecs compress images by selecting elements from a shared randomness by the encoder and the decoder. Fast methods such as Turbo-DDCM score atoms using latent residual, restricting direct control to the latent-space quadratic objective. Optimizing the zero-shot diffusion-based image compression method on a general class of objective functions has not been fully studied yet. DDCM and Turbo-DDCM explored some compression with regional priority and perception optimizing encoding methods, but those methods don't give a direction toward a scalable general compression method for arbitrary loss function. We introduce LOONA, which enables the efficient selection of noise from any differentiable objectives. LOONA does so by determining the optimal linear function for the given objective, which makes our computational complexity not scale with $K$, avoiding exhaustive evaluation of candidate combinations. Each guided step computes one Vector-Jacobian product, with neural evaluation cost independent of codebook size and the number of selected atoms. LOONA retains Turbo-DDCM's bitstream format and frozen decoder, transmits no additional side information, and requires no retraining. We experiment on objectives as pixel-level fidelity loss, semantic preservation loss and perceptual loss and demonstrate that LOONA successfully optimizes the given objectives. From these various choice of compression objectives, LOONA improves the rate–distortion Pareto-frontier, with improved full-resolution region control, semantic preservation, and a tunable perception-distortion tradeoff.