Robin Jenkin

Yuzhuo Ren, Yining Deng, David Pajak et al.

In a surround view system, the image color and tone captured by multiple cameras can be different due to cameras applying auto white balance (AWB), global tone mapping (GTM) individually for each camera. The color and brightness along stitched seam location may look discontinuous among multiple cameras which impacts overall stitched image visual quality. To improve the color transition between adjacent cameras in stitching algorithm, we propose harmonization algorithm which applies before stitching to adjust multiple cameras' color and tone so that stitched image has smoother color and tone transition between adjacent cameras. Our proposed harmonization algorithm consists of AWB harmonization and GTM harmonization leveraging Image Signal Processor (ISP)'s AWB and GTM metadata statistics. Experiment result shows that our proposed algorithm outperforms global color transfer method in both visual quality and computational cost.

Edward W. S. Fry, Sophie Triantaphillidou, Robin B. Jenkin et al.

Spatial image quality metrics designed for camera systems generally employ the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS), and a visual contrast detection model. Prior art indicates that scene-dependent characteristics of non-linear, content-aware image processing are unaccounted for by MTFs and NPSs measured using traditional methods. We present two novel metrics: the log Noise Equivalent Quanta (log NEQ) and Visual log NEQ. They both employ scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures, which account for signal-transfer and noise scene-dependency, respectively. We also investigate implementing contrast detection and discrimination models that account for scene-dependent visual masking. Also, three leading camera metrics are revised that use the above scene-dependent measures. All metrics are validated by examining correlations with the perceived quality of images produced by simulated camera pipelines. Metric accuracy improved consistently when the SPD-MTFs and SPD-NPSs were implemented. The novel metrics outperformed existing metrics of the same genre.

Edward W. S. Fry, Sophie Triantaphillidou, Robin B. Jenkin et al.

The Modulation Transfer Function (MTF) and the Noise Power Spectrum (NPS) characterize imaging system sharpness/resolution and noise, respectively. Both measures are based on linear system theory but are applied routinely to systems employing non-linear, content-aware image processing. For such systems, MTFs/NPSs are derived inaccurately from traditional test charts containing edges, sinusoids, noise or uniform tone signals, which are unrepresentative of natural scene signals. The dead leaves test chart delivers improved measurements, but still has limitations when describing the performance of scene-dependent systems. In this paper, we validate several novel scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures that characterize, either: i) system performance concerning one scene, or ii) average real-world performance concerning many scenes, or iii) the level of system scene-dependency. We also derive novel SPD-NPS and SPD-MTF measures using the dead leaves chart. We demonstrate that all the proposed measures are robust and preferable for scene-dependent systems than current measures.