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The speedup of parameter estimation is an active field of research in gravitational-wave data analysis. In this paper, we present GP15, a deep-learning method that merges residual networks and normalizing flows into a general-purpose, image-based estimator of binary black hole (BBH) parameters. Building on our early work, we map BBH spectrograms from the Advanced LIGO and Advanced Virgo detectors to color channels in an RGB image amenable to be processed with residual networks. GP15 is trained on simulated data for BBH mergers obtained with the IMRPhenomXPHM waveform approximant and tested for all three-detector events from the gravitational-wave transient catalogs GWTC-3 and GWTC-2.1 reported by the LIGO-Virgo-KAGRA (LVK) Collaboration. Overall, our model yields good agreement with the LVK results over most parameters. Our simple model can produce large amounts of posterior samples on the order of a second, complementing existing approaches with normalizing flows based on time or frequency representation of gravitational-wave data. We also discuss current shortcomings of our model and possible improvements for future extensions (e.g., including noise conditioning from the detectors' power spectral density or splitting the parameter space into intrinsic and extrinsic subspaces).