  High dynamic range (HDR) capture and display have seen significant growth in
popularity driven by the advancements in technology and increasing consumer
demand for superior image quality. As a result, HDR image compression is
crucial to fully realize the benefits of HDR imaging without suffering from
large file sizes and inefficient data handling. Conventionally, this is
achieved by introducing a residual/gain map as additional metadata to bridge
the gap between HDR and low dynamic range (LDR) images, making the former
compatible with LDR image codecs but offering suboptimal rate-distortion
performance. In this work, we initiate efforts towards end-to-end optimized HDR
image compression for perceptually optimal storage and display. Specifically,
we learn to compress an HDR image into two bitstreams: one for generating an
LDR image to ensure compatibility with legacy LDR displays, and another as side
information to aid HDR image reconstruction from the output LDR image. To
measure the perceptual quality of output HDR and LDR images, we use two
recently proposed image distortion metrics, both validated against human
perceptual data of image quality and with reference to the uncompressed HDR
image. Through end-to-end optimization for rate-distortion performance, our
method dramatically improves HDR and LDR image quality at all bit rates.
