Complexity-Aware Rate-Distortion Analysis of Classical and Neural Image Codecs
K. Kiruthika
Digital Signal Processing and Artificial Intelligence for Automatic Learning, Vol. 5, No. 1, 2026.
DOI: https://doi.org/10.6025/dspaial/2026/5/1/1-17
Link: https://www.dline.info/dspai/fulltext/v5n1/dspaiv5n1_1.pdf
Abstract:
This study presents a complexity aware evaluation framework for comparing classical and neural image
codecs across diverse content regimes. As video compression becomes critical for resource constrained
edge IoT devices, conventional benchmarking often relies on aggregate metrics that obscure systematic
performance variations driven by content complexity. Utilizing the Google Open Images Dataset (V7), we
stratified approximately 125,000 images into three balanced complexity bins low, medium, and high using
original file size as a proxy for spatial entropy via quantile based thresholds. We evaluated classical anchors
(JPEG, AV1, VVC) and a representative neural codec, computing BD-Rate and BD-PSNR metrics separately
for each bin to ensure statistically stable estimation.
Empirical results reveal that neural codecs achieve substantial bitrate savings on low-complexity content (-
32% versus JPEG), but advantages diminish significantly in high complexity regimes (-11%). Conversely,
traditional codecs exhibit more robust performance across varying entropy levels. Statistical validation
confirms these trends are not sampling artifacts. These findings establish content complexity as a first order
variable governing codec behavior, indicating that current neural architectures struggle with high frequency
details compared to hand engineered transforms. Consequently, we advocate for complexity stratified
evaluation protocols as standard practice to prevent misleading performance assessments. The study
highlights the necessity for complexity adaptive neural architectures and dynamic codec selection in green
multimedia systems to maximize energy efficiency in heterogeneous edge environments. Future research
must prioritize content conditioned rate allocation to bridge the gap between probabilistic bitstreams and
dynamic network conditions.