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High Dynamic Range (HDR) imaging is gaining increased attention due to itsrealistic content, for not only regular displays but also smartphones. Beforesufficient HDR content is distributed, HDR visualization still relies mostly onconverting Standard Dynamic Range (SDR) content. SDR images are oftenquantized, or bit depth reduced, before SDR-to-HDR conversion, e.g. for videotransmission. Quantization can easily lead to banding artefacts. In somecomputing and/or memory I/O limited environment, the traditional solution usingspatial neighborhood information is not feasible. Our method includes noisegeneration (offline) and noise injection (online), and operates on pixels ofthe quantized image. We vary the magnitude and structure of the noise patternadaptively based on the luma of the quantized pixel and the slope of theinverse-tone mapping function. Subjective user evaluations confirm the superiorperformance of our technique.

Adaptive Dithering Using Curved Markov-Gaussian Noise in the Quantized Domain for Mapping SDR to HDR Image