This paper presents a dataset-level evaluation of six lossless compression and data transformation techniques applied to visual-cryptographic (VC) shares derived from QR codes. We processed 40,000 QR samples, comprising 10,000 QR images (Versions 1-4, 2,500 per version), 10,000 QR images (Versions 1-10, 1,000 per version across ten application domains), and 20,000 augmented QR images (with noise, rotation, shear, cropping, and brightness variations). Each QR image is converted to VC share (share1), flattened to a bitstream, and evaluated under traditional compression techniques such as Run Length Encoding (RLE), Huffman Coding, Lempel Ziv-Welch (LZW), and data transformation techniques such as Binary-to-Integer, Base64 Encoding, and (BWT + MTF + Huffman Coding) Burrows Wheeler Transform (BWT), Move-To-Front (MTF), and Huffman Coding as a combined pipeline. Our experiments report Shannon entropy, compressed character count, compressed character count percentage, compression time, decompression time, memory usage, peak memory, lossless fidelity, metadata, payload size, storage size, and compression ratio. Empirical results show near-maximal entropy in QR-derived VC data (�0.99), providing constraints on compression performance for traditional algorithms. Base64 consistently yields the most efficient character-count reduction, averaging 83 across both clean and augmented datasets. Making it most suitable for reducing visual cryptography shares to fit QR code character limit, while accepting 33 increase in bit-level storage. This work contributes a reproducible pipeline, a generalized dataset, and a benchmark reference for compression research on a highly randomized binary dataset. © 2013 IEEE.