Generally, image compression algorithms are developed, and implemented, without taking into account the distortions injected into the process by the image-gathering and the image-display systems. Often, these distortions degrade the quality of the displayed image more than those due to coding and quantization. We assess the whole coding process - from image capture to image display - using information-theoretical analysis. The restoration procedure which we develop for (Discrete Cosine) transform coded images takes into account not only the quantization errors introduced by the coding, but also the aliasing and blurring errors due to (non-ideal) image gathering and display. This procedure maximizes the information content of the image-gathering and the fidelity of the resultant restorations.