Optimized tone mapping with LDR image quality constraint for backward-compatible high dynamic range image and video coding

Abstract

Backward compatibility to low dynamic range (LDR) displays is an important requirement for high dynamic range (HDR) image and video coding in order to enable a successful transition to HDR technology. In a recent work [1], an optimized solution for tone mapping and inverse tone mapping of HDR images is achieved in terms of mean square error (MSE) of the logarithm of luminance values of HDR image pixels for backward-compatible compression. Although this pioneer optimization approach provides a well settled mathematical framework for tone mapping, one of its important shortcomings is not to take the quality of the resulting LDR images into account during the formulation. In this paper, we include the LDR image quality as a constraint to optimization problem and develop a methodology to compromise the trade-off between HDR image quality and LDR image quality during HDR image and video coding. The developed methodology is verified on HDR images by showing the increase (decrease) in the quality of generated LDR images while losing (gaining) from the rate-distortion performance of HDR image coding.

L2 norm optimization of tone mapping for two layer lossless coding of HDR images

“…In this paper, we develop a backward compatible lossless coding of HDR images expressed in a floating point data format via introducing the optimization procedure in [17] to the existing method in Sec. II-C.…”

“…1 was reduced. On the contrary, in [17], a constraint of ||e Q || 2 with a given constant C Q was added asĥ…”

“…However, image quality of the LDR image was not considered. Subsequently, a constraint of the LDR image quality was added in [17] to the optimization procedure. As a result, coding performance was significantly improved in lossy coding of the LDR image.…”

“…Even though a user can use arbitrary tone mapping function to produce an LDR from the HDR image, there is no room to optimize it according to the input image. In this paper, we introduce one more function and determine its shape based on the L2 norm optimization in [17], so that quality of the LDR image is increased.…”

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

“…In this sense, HDR images are capable of capturing the luminance of real scenes from extremely dark areas, with 10 −6 cd/m 2 , to very bright areas, with 10 8 cd/m 2 [22]. This is, nevertheless, a relatively recent area, with around a decade of existence [18,23].…”