Content created in High Dynamic Range (HDR) and Wide Color Gamut (WCG) is becoming more ubiquitous, driving the need for reliable tools for evaluating the quality across the imaging ecosystem. One of the simplest techniques to measure the quality of any video system is to measure the color errors. The traditional color difference metrics such as ΔE00 and the newer HDR specific metrics such as ΔEZ and ΔEITP compute color difference on a pixel-by-pixel basis which do not account for the spatial effects (optical) and active processing (neural) done by the human visual system. In this work, we improve upon the per-pixel ΔEITP color difference metric by performing a spatial extension similar to what was done during the design of S-CIELAB. We quantified the performance using four standard evaluation procedures on four publicly available HDR and WCG image databases and found that the proposed metric results in a marked improvement with subjective scores over existing per-pixel color difference metrics.
Barcodes and watermarks offer different trade-offs for carrying data through a displayed image. Barcodes offer robust detection and decoding with high data capacity but are visually obtrusive. Watermarks are imperceptible but their detection and decoding is less robust, and they offer lower data capacity. Image-barcodes straddle this trade-off by attempting to reduce perceptual obtrusiveness compared with conventional barcodes, while minimally compromising data robustness. We propose an image-barcode for display applications that is simple, yet novel. The proposed method encodes the data into a monochrome barcode and embeds it into a suitably chosen region in the blue/red channel of a displayed image. The reduced sensitivity of the human visual system to changes in these channels (particularly the blue channel) reduces the perceptual impact of the image barcode compared with conventional black and white barcodes. The data can, however, still be robustly recovered from a typical color image capture of the displayed image-barcode by decoding only the channels with the embedding. We assess visual distortion and robustness of data recovery for the proposed method and experimentally compare against a baseline black and white barcode for two barcode modes representative of potential usage scenarios. Visual distortion for the proposed method is significantly better. Under typical settings, the proposed method introduces a mean SCIELAB-CIEDE2000 distortion of ΔE = 0.39 for the blue channel embedding and ΔE = 0.35 for the red channel embedding, compared with ΔE = 0.59 for the baseline method. For data recovery, the blue and red channel embeddings using the proposed method match the 100% decoding success rate and synchronization success rate for the baseline method, although, the pre-error-correction observed mean bit error rate of 0.047% (0.08%) for the blue (red) channel embedding is marginally worse than the performance of the baseline method.