Methods for improving the backward compatible High Dynamic Range compression

David Gommelet 1, 2
2 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In recent years, video content evolved very quickly. Indeed, televisions (TV) quickly evolved to Ultra High Definition (UHD), High Frame Rate (HFR) or stereoscopy (3D). The recent trend is towards High Dynamic range (HDR). These new technologies allow the reproduction of much brighter images than for actual displays. Each of these improvements represents an increase in storage cost and therefore requires the creation of new video compression standards, always more efficient. The majority of consumers are currently equipped with Standard Dynamic Range (SDR) displays, that cannot handle HDR content. Consumers will slowly renew their display to an HDR one and it is therefore of great importance to deliver an HDR signal that can be decoded by both SDR and HDR displays. Such backward compatibility is provided by a tool called Tone Mapping Operator (TMO) which transforms an HDR content into an SDR version. In this thesis, we explore new methods to improve the backward compatible HDR compression. First, we design a Tone Mapping to optimize scalable compression scheme performances where a base and an enhancement layer are sent to reconstruct the SDR and HDR content. It is demonstrated that the optimum TMO only depends on the SDR base layer and that the minimization problem can be separated in two consecutive minimization steps. Based on these observations, we then propose another TMO designed to optimize the performances of compression schemes using only a base layer but with an enhanced and more precise model. Both of these works optimize TMO for still images. Thereafter, this thesis focuses on the optimization of video-specific TMO. However, we demonstrate that using a weighted prediction for the SDR compression is as good or even better than using a temporally optimized TMO. Therefore, we proposed a new weighted prediction algorithm and new weighted prediction modes to handle more efficiently the large diversity of brightness variations in video sequences.
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Submitted on : Thursday, December 6, 2018 - 5:14:08 PM
Last modification on : Friday, May 3, 2019 - 4:27:43 AM
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David Gommelet. Methods for improving the backward compatible High Dynamic Range compression. Image Processing [eess.IV]. Université Rennes 1, 2018. English. ⟨NNT : 2018REN1S033⟩. ⟨tel-01947401⟩

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