Steganography, the art of concealing information within another message or physical object to evade detection, has potential applications across multiple digital content types, including text, photos, videos, and audio. The hidden data size significantly influences the difficulty of detection. Conversely, the data amount that can be concealed within an image is largely dependent on the cover image dimensions, a concept often overlooked by steganographers. Despite numerous attempts to improve embedding capacity, the quality of generated stego-images remains subpar, and embedding capacity continues to be restricted by the cover image size. This study introduces an image steganography approach, leveraging double density dual tree wavelet transform (DDDT-DWT), designed to enhance capacity while preserving optimal quality. The performances of discrete wavelet transform (DWT), double density DWT (DD-DWT), and double density dual tree DWT (DDDT-DWT) are implemented, evaluated, and comparatively assessed. Key performance parameters, such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE), are calculated, guiding the selection of the most efficient methodology. The stego-image quality is also measured using the Structural Similarity Index Metric (SSIM). Experimental results indicate that the proposed DDDT-DWT-based method yields superior imperceptibility for the stego image, with a PSNR of 47.8582 and an SSIM of 0.9945. This advancement in steganography presents opportunities for increasingly undetectable and efficient data concealment.