ImageQR Studio: A Protected Module Framework for Logo Reconstruction and Multi-Decoder Evaluation

ImageQR Studio reconstructs supplied artwork across a QR module field while keeping its encoded Booleanmatrix unchanged. The framework combines source-resolution foreground analysis, alpha and edge evidence, structural importance, protected-module mapping, hue-preserving lightness adjustment, supersampled dot rendering, and exact-payloaddecoder feedback. It supports automatic version selection, fixed Versions 10, 20, 30, and 40, a local browser interface, acommand-line interface, and a conventional no-logo fallback. We extend the earlier framework description with a recordedsoftware study of 30 supplied logo files, five payloads, and five version policies: 750 logo jobs and 25 standard controls. At1024-pixel output, 706 of 775 scheduled jobs were accepted by the original pipeline; 69 failed, comprising 31 expected capacity rejections, 37 styled-validation failures, and one memory-allocation error. A separate sequential recovery accepted thememory case without changing the original totals. All 743 produced last candidates were evaluated with ZXing C++, ZBar,and OpenCV under clean and five fixed degradation conditions, yielding 13,374 final-candidate decoder measurements. Cleanlogo decoding was 78.16%, 75.66%, and 71.63%, respectively. Only 29 of 719 logo candidates passed every condition inall three decoders, showing that pipeline acceptance is weaker than broad compatibility. The final automated suite passedall 35 tests, and four selected repeats produced identical PNG files. A separate 4096-pixel pilot, descriptive family-clusteruncertainty, retry histories, image diagnostics, and operational failures are retained. Physical scanning and human recognition remain unmeasured. Future work prioritizes device validation, controlled ablations, calibrated visual metrics, and aconstrained lightweight learned importance model.