Error correction codes of suitable redundancy are used for ensuring perfect data recovery in noisy channels. For iterative decoding based methods, the decoder needs to be initialized with proper confidence values, called the log likelihood ratios (LLRs), for all the embedding locations. If these confidence values or LLRs are accurately initialized, the decoder converges at a lower redundancy factor, thus leading to a higher effective hiding rate. Here, we present an LLR allocation method based on the image statistics, the hiding parameters and the noisy channel characteristics. It is seen that this image-dependent LLR allocation scheme results in a higher data-rate, than using a constant LLR across all images. The data-hiding channel parameters are learned from the image histogram in the discrete cosine transform (DCT) domain using a linear regression framework. We also show how the effective data-rate can be increased by suitably increasing the erasure rate at the decoder.