Local Inverse Tone Curve Learning for High Dynamic Range Image Scalable Compression

Abstract : This paper presents a scalable high dynamic range (HDR) image coding scheme in which the base layer is a low dynamic range (LDR) version of the image that may have been generated by an arbitrary Tone Mapping Operator (TMO). No restriction is imposed on the TMO, which can be either global or local, so as to fully respect the artistic intent of the producer. Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template based Inter Layer Prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for blockwise ILP. In addition, this paper shows that a linear adjustment of the initially predicted block can further improve the overall coding performance by using an efficient encoding scheme of the scaling parameters. Our experiments have shown an average bitrate saving of 47% on the HDR enhancement layer, compared to previous local ILP methods.
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https://hal.inria.fr/hal-01204722
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Submitted on : Tuesday, October 20, 2015 - 3:57:24 PM
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Mikael Le Pendu, Christine Guillemot, Dominique Thoreau. Local Inverse Tone Curve Learning for High Dynamic Range Image Scalable Compression. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2015, 24 (12), pp.5753-5763. ⟨10.1109/TIP.2015.2483899⟩. ⟨hal-01204722⟩

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