Beyond Rate-Distortion: A Complexity-Aware Evaluation of Image Codecs Reveals INR-Based Approaches as Pareto- Optimal for Edge Deployment
Hathairat Ketmaneechairat
Digital Signal Processing and Artificial Intelligence for Automatic Learning, Vol. 5, No. 1, 2026.
DOI: https://doi.org/10.6025/dspaial/2026/5/1/32-45
Link: https://www.dline.info/dspai/fulltext/v5n1/dspaiv5n1_3.pdf
Abstract:
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 (<5 ms), delivering competitive reconstruction quality with orders of magnitude
lower decoding latency and linear complexity scaling with resolution unlike autoregressive methods'
superlinear growth. Pareto front analysis confirms that RD optimal operating points are frequently dominated
in RDC space once complexity constraints are imposed. Complexity constrained evaluations further
demonstrate that codec superiority is budget dependent: INR dominates at low latency budgets, hyperprior
models become competitive at moderate budgets (5-20 ms), and only at high budgets (>20 ms) do
autoregressive methods justify their computational cost. These findings advocate for a paradigm shift toward
RDC aware codec design and evaluation, positioning INR-based compression as a compelling solution for
energy efficient, low latency streaming on heterogeneous edge devices without sacrificing perceptual fidelity.