Beyond Rate-Distortion: A Complexity-Aware Evaluation of Image Codecs Reveals INR-Based Approaches as Pareto- Optimal for Edge Deployment

This study challenges the conventional rate distortion (RD) paradigm for image codec evaluation by demonstrating its inadequacy in resource constrained, real world deployments.We introduce a unified rate distortion complexity (RDC) framework that explicitly incorporates decoding complexity as a first class evaluation dimension alongside bitrate and reconstruction quality.Through systematic analysis across classical (JPEG, BPG, VVC-intra), learned neural (hyperprior, autoregressive), and implicit neural representation (INR) codecs on a complexity stratified dataset, we reveal critical trade offs invisible to RD only assessment.Our results show that autoregressive neural codecs, while achieving state of the art RD performance, incur prohibitive decoding latency due to inherently sequential entropy decoding rendering them impractical for edge devices.Conversely, INR based approaches emerge as Pareto optimal under tight computational budgets (