Electronic Devices Ricardo Rodríguez Jorge 15 1 2026 https://doi.org/10.6025/ed/2026/15/1/20-32 https://www.dline.info/ed/fulltext/v15n1/edv15n1_2.pdf This study introduces a comprehensive entropy characterization and complexity-aware benchmarking framework for residual data generated in learned image and video compression pipelines. Analyzing a large corpus of raw, content agnostic residual symbols at byte level granularity, we uncover critical statistical properties that challenge conventional entropy modeling assumptions. Empirical analysis reveals substantial heterogeneity across residuals, with entropy values spanning 3.5-5.0 bits per symbol (mean ±1 bit standard deviation) significantly below the theoretical maximum of 8 bits indicating rich statistical redundancy exploitable for compression. Symbol distributions exhibit pronounced heavy tailed characteristics, with only 10-15 dominant symbols accounting for 50-70% of occurrences, invalidating Gaussian or Laplacian assumptions pervasive in traditional codecs. Bit plane analysis further demonstrates extreme sparsity (95- 98%) in higher order planes, confirming residuals concentrate tightly around zero a signature of effective prediction. Crucially, entropy exhibits strong non stationarity across content instances, naturally stratifying into low, medium, and high complexity regimes. Rate distortion complexity (RDC) evaluation across hyperprior, autoregressive, and transformer based entropy models quantifies fundamental trade offs: while transformer architectures achieve up to 1.36 dB gains over hyperpriors at low bitrates, they impose >12× computational overhead, revealing diminishing returns beyond moderate complexity budgets. The autoregressive approach delivers optimal balance (0.78 dB gain at 5.2× complexity). Our dataset and analysis framework provide a content agnostic benchmark for evaluating entropy modeling efficacy independent of codec specific preprocessing, establishing that adaptive, complexity aware strategies not static global models are essential for efficient learned compression deployment across diverse hardware constraints.