A method for image metadata optimization, the method comprising: determining a plurality of sets of training images and a plurality of sets of targeted values for one or more image metadata parameters, each set of targeted values in the plurality of sets of targeted values corresponding to a respective set of one or more training images in the plurality of sets of training images; extracting a plurality of sets of image data features of one or more selected image data feature types from the plurality of sets of training images, each set of image data features in the plurality of sets of image data features corresponding to a respective set of one or more training images in the plurality of sets of training images; applying the plurality of sets of image data features of the one or more selected image data feature types and the plurality of sets of targeted values for the one or more image metadata parameters to training one or more prediction models for optimizing the one or more image metadata parameters; using the one or more prediction models to generate one or more optimized values for the one or more image metadata parameters for one or more non-training images; causing the one or more optimized values for the one or more image metadata parameters to be used in rendering the one or more non-training images; the method being characterized in that the one or more non-training images represent one or more standard dynamic range images; wherein the plurality of sets of training images represent high dynamic range images; the method further being characterized by it comprising: extracting a set of non-training SDR image data features of the one or more selected image data feature types from the one or more non-training images; generating a set of non-training HDR image data features of the one or more selected image data feature types from the set of non-training SDR image data features of the one or more selected image data feature types extracted from the one or more non-training images; causing the one or more prediction models to use the set of non-training HDR image data features of the one or more selected image data feature types to generate the one or more optimized values for the one or more image metadata parameters for the one or more non-training images. determining a plurality of sets of training images and a plurality of sets of targeted values for one or more image metadata parameters, each set of targeted values in the plurality of sets of targeted values corresponding to a respective set of one or more training images in the plurality of sets of training images; extracting a plurality of sets of image data features of one or more selected image data feature types from the plurality of sets of training images, each set of image data features in the plurality of sets of image data features corresponding to a respective set of one or more training images in the plurality of sets of training images; applying the plurality of sets of image data features of the one or more selected image data feature types and the plurality of sets of targeted values for the one or more image metadata parameters to training one or more prediction models for optimizing the one or more image metadata parameters; using the one or more prediction models to generate one or more optimized values for the one or more image metadata parameters for one or more non-training images; causing the one or more optimized values for the one or more image metadata parameters to be used in rendering the one or more non-training images; the method being characterized in that the one or more non-training images represent one or more standard dynamic range images; wherein the plurality of sets of training images represent high dynamic range images; the method further being characterized by it comprising: extracting a set of non-training SDR image data features of the one or more selected image data feature types from the one or more non-training images; generating a set of non-training HDR image data features of the one or more selected image data feature types from the set of non-training SDR image data features of the one or more selected image data feature types extracted from the one or more non-training images; causing the one or more prediction models to use the set of non-training HDR image data features of the one or more selected image data feature types to generate the one or more optimized values for the one or more image metadata parameters for the one or more non-training images. extracting a set of non-training SDR image data features of the one or more selected image data feature types from the one or more non-training images; generating a set of non-training HDR image data features of the one or more selected image data feature types from the set of non-training SDR image data features of the one or more selected image data feature types extracted from the one or more non-training images; causing the one or more prediction models to use the set of non-training HDR image data features of the one or more selected image data feature types to generate the one or more optimized values for the one or more image metadata parameters for the one or more non-training images.