List of Published Research

A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images

Joss Whittle and Mark W. Jones. Presented at CGVC 2018 - Swansea, Wales.

In Full-Reference Image Quality Assessment (FR-IQA) images are compared with ground truth images that are known to be of high visual quality. These metrics are utilized in order to rank algorithms under test on their image quality performance. Throughout the progress of Monte Carlo rendering processes we often wish to determine whether images being rendered are of sufficient visual quality, without the availability of a ground truth image. In such cases FR-IQA metrics are not applicable and we instead must utilise No-Reference Image Quality Assessment (NR-IQA) measures to make predictions about the perceived quality of unconverged images. In this work we propose a deep learning approach to NR-IQA, trained specifically on noise from Monte Carlo rendering processes, which significantly outperforms existing NR-IQA methods and can produce quality predictions consistent with FR-IQA measures that have access to ground truth images.

Cite as:

    author    = "Whittle, Joss and Jones, Mark W.", 
    booktitle = "Computer Graphics and Visual Computing (CGVC) 2018", 
    title     = "A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images", 
    year      = "2018", 
    month     = "Sep",
    pages     = {23--31}, 
    doi       = {10.2312/cgvc.20181204} 

Analysis of reported error in Monte Carlo rendered images

Joss Whittle, Mark W. Jones, and Rafał Mantiuk. Published in The Visual Computer, June 2017, Volume 33, Issue 6–8, pp 705–713. Presented at CGI 2017 - Yokohama, Japan.

Evaluating image quality in Monte Carlo rendered images is an important aspect of the rendering process as we often need to determine the relative quality between images computed using different algorithms and with varying amounts of computation. The use of a gold-standard, reference image, or ground truth is a common method to provide a baseline with which to compare experimental results. We show that if not chosen carefully, the quality of reference images used for image quality assessment can skew results leading to significant misreporting of error. We present an analysis of error in Monte Carlo rendered images and discuss practices to avoid or be aware of when designing an experiment.

Cite as:

    author  = "Whittle, Joss and Jones, Mark W. and Mantiuk, Rafa{\l}",
    title   = "Analysis of reported error in Monte Carlo rendered images",
    journal = "The Visual Computer",
    year    = "2017",
    month   = "Jun",
    day     = "01",
    volume  = 33,
    number  = 6,
    pages   = {705--713},
    issn    = {1432-2315},
    url     = {},
    doi     = {10.1007/s00371-017-1384-7}

Implementing generalized deep-copy in MPI

Joss Whittle, Rita Borgo and Mark W. Jones. Published in PeerJ-CS, 2016.

In this paper, we introduce a framework for implementing deep copy on top of MPI. The process is initiated by passing just the root object of the dynamic data structure. Our framework takes care of all pointer traversal, communication, copying and reconstruction on receiving nodes. The benefit of our approach is that MPI users can deep copy complex dynamic data structures without the need to write bespoke communication or serialize/deserialize methods for each object. These methods can present a challenging implementation problem that can quickly become unwieldy to maintain when working with complex structured data. This paper demonstrates our generic implementation, which encapsulates both approaches. We analyze the approach with a variety of structures (trees, graphs (including complete graphs) and rings) and demonstrate that it performs comparably to hand written implementations, using a vastly simplified programming interface. We make the source code available completely as a convenient header file.

Cite as:

    title   = "Implementing generalized deep-copy in MPI",
    author  = "Whittle, Joss and Borgo, Rita and Jones, Mark W.",
    journal = "PeerJ Computer Science",
    year    = "2016",
    month   = "Nov",
    volume  = 2,
    pages   = {e95},
    issn    = {2376-5992},
    url     = {},
    doi     = {10.7717/peerj-cs.95}