Foreword: Special section on visual media production

1. Introduction The Conference on Visual Media Production (CVMP 2014) was the eleventh in a conference series that is now established as a leading venue for the creative industry and academics interest in visual computing. As always, the programme comprised a rich mix of invited speakers, peer reviewed papers, and special sessions. In 2014 we enjoyed an exceptional set of submissions. Both papers contribute both academically and industrially to a vibrant research area. This year we received 32 submissions, and accepted only 11 papers for oral presentation making the selection process especially tough. The review process itself was double-blind, comprising at least 3 internationally expert reviewers whose comments and scores determine the final outcome for each paper. In cases where one of the chairs was conflicted with a submission, the outcome was determined separately by others on the committee. Of those papers finally accepted, only the two best papers (as scored by the review process) have made it through to this special section of Elsevier Computers and Graphics (C&G) showcasing the very best work at CVMP 2014. In an industry where cutting edge computer graphics is the lifeblood of innovation and digital entertainment content delivery, we are delighted to have C&G showcase the best of the innovation at CVMP 2014. We are grateful to Prof. Joaquim Jorge and all in the C&G journal office for facilitating the publication of this special section. Tone mapping is needed to view HDR images, but successful results can be hard to obtain. In our first paper, Gao et al. show us how to automatically tune tone-mapping parameters, using visual saliency as a constraint. Rather than tediously finding the right settings, users can call upon Automated parameter tuning for tone mapping using visual saliency [1] to make their work easier. In our second paper Barber et al. examine the problem of camera blur. Clean video is needed for many applications in the creative sector, and high quality cleaning of blurred video requires the motion of the camera to be known. The paper we carry, Inferring changes in intrinsic parameters from motion blur [2], contributes by estimating changes in focal length using motion blur. It has been a pleasure to organise CVMP, and to compile this special section. We are sure you will enjoy it.