WideR: Rate-adaptive Balancing for Wide-range Rate-Distortion-Perception Control in Neural Image Compression

Neural image compression has advanced both in reducing distortion and improving perceptual quality, yet improving one can compromise the other at a fixed bitrate. Distortion-perception (D-P) controllable codecs address this trade-off by adjusting the balance between them within a single codec. Existing methods commonly train the codec for rate-distortion performance and then freeze the encoder while adapting the decoder to other preferences. Such adaptation changes how the encoded information is used, but cannot change what is encoded. Our study reveals that distortion-oriented representations incur a perception penalty that grows toward lower rates, whereas perception-oriented representations incur a distortion penalty that grows toward higher rates, even after decoder adaptation. To alleviate these cross-endpoint penalties, we propose rate-adaptive D-P objective balancing, which optimizes the codec with both reconstruction objectives and progressively shifts their relative weighting from perception to distortion as rate increases. We instantiate our method in WideR, a rate-distortion-perception controllable codec with a pretrained one-step diffusion prior, variable-rate coding, and a decoder-side D-P control mechanism. Across diverse datasets, WideR demonstrates broader D-P control than EGIC, the strongest baseline among the compared methods, achieving BD-rate gains of up to -60.0% in FID for perception-oriented reconstruction and -5.6% in PSNR for distortion-oriented reconstruction. It also achieves BD-rate gains of up to -20.3% in rate-PSNR at matched FID and -70.6% in rate-FID at matched PSNR. Code will be released upon publication.