An image rendering method for rendering a pixel at a viewpoint, comprising the steps of: for a first element of a virtual scene, having a predetermined surface at a position within that scene, evaluating whether to render a pixel corresponding to the first element by using an illuminance output generated by at least one machine learning system (600-D) having been trained to generate the illuminance output, wherein the illuminance output is representative of the lighting of the predetermined surface at the position, or by using an alternative rendering approach, and rendering the pixel according the result of the evaluating step; wherein the evaluating step comprises: obtaining a confidence value output by the at least one machine learning system indicative of the accuracy of the illuminance output, the at least one machine learning system having been trained to generate the confidence value in conjunction with the illuminance output; and the rendering step comprises: using the alternative rendering approach if the confidence value does not reach a predetermined threshold value, and using the at least one machine learning system if the confidence value reaches the predetermined threshold value; wherein, when the rendering step comprises rendering the pixel using the at least one machine learning system, the rendering step further comprises: using respective machine learning systems that have each been trained for a respective one of a plurality of illuminance contributing components of the image; for each of the plurality of illuminance contributing components of the image, combining the illuminance output of the respective machine learning system with a respective distribution function that characterises an interaction of light with the predetermined surface to generate a pixel value corresponding to the first element of the virtual scene as illuminated at the position; and combining the respective generated pixel values to create a final combined pixel value incorporated into a rendered image for display. for a first element of a virtual scene, having a predetermined surface at a position within that scene, evaluating whether to render a pixel corresponding to the first element by using an illuminance output generated by at least one machine learning system (600-D) having been trained to generate the illuminance output, wherein the illuminance output is representative of the lighting of the predetermined surface at the position, or by using an alternative rendering approach, and rendering the pixel according the result of the evaluating step; wherein the evaluating step comprises: obtaining a confidence value output by the at least one machine learning system indicative of the accuracy of the illuminance output, the at least one machine learning system having been trained to generate the confidence value in conjunction with the illuminance output; and the rendering step comprises: using the alternative rendering approach if the confidence value does not reach a predetermined threshold value, and using the at least one machine learning system if the confidence value reaches the predetermined threshold value; wherein, when the rendering step comprises rendering the pixel using the at least one machine learning system, the rendering step further comprises: using respective machine learning systems that have each been trained for a respective one of a plurality of illuminance contributing components of the image; for each of the plurality of illuminance contributing components of the image, combining the illuminance output of the respective machine learning system with a respective distribution function that using the alternative rendering approach if the confidence value does not reach a predetermined threshold value, and using the at least one machine learning system if the confidence value reaches the predetermined threshold value; wherein, when the rendering step comprises rendering the pixel using the at least one machine learning system, the rendering step further comprises: using respective machine learning systems that have each been trained for a respective one of a plurality of illuminance contributing components of the image; for each of the plurality of illuminance contributing components of the image, combining the illuminance output of the respective machine learning system with a respective distribution function that using respective machine learning systems that have each been trained for a respective one of a plurality of illuminance contributing components of the image; for each of the plurality of illuminance contributing components of the image, combining the illuminance output of the respective machine learning system with a respective distribution function that characterises an interaction of light with the predetermined surface to generate a pixel value corresponding to the first element of the virtual scene as illuminated at the position; and combining the respective generated pixel values to create a final combined pixel value incorporated into a rendered image for display.