  In this work, we address the task of SDR videos to HDR
videos(SDRTV-to-HDRTV). Previous approaches use global feature modulation for
SDRTV-to-HDRTV. Feature modulation scales and shifts the features in the
original feature space, which has limited mapping capability. In addition, the
global image mapping cannot restore detail in HDR frames due to the luminance
differences in different regions of SDR frames. To resolve the appeal, we
propose a two-stage solution. The first stage is a hierarchical Dynamic Context
feature mapping (HDCFM) model. HDCFM learns the SDR frame to HDR frame mapping
function via hierarchical feature modulation (HME and HM ) module and a dynamic
context feature transformation (DCT) module. The HME estimates the feature
modulation vector, HM is capable of hierarchical feature modulation, consisting
of global feature modulation in series with local feature modulation, and is
capable of adaptive mapping of local image features. The DCT module constructs
a feature transformation module in conjunction with the context, which is
capable of adaptively generating a feature transformation matrix for feature
mapping. Compared with simple feature scaling and shifting, the DCT module can
map features into a new feature space and thus has a more excellent feature
mapping capability. In the second stage, we introduce a patch
discriminator-based context generation model PDCG to obtain subjective quality
enhancement of over-exposed regions. PDCG can solve the problem that the model
is challenging to train due to the proportion of overexposed regions of the
image. The proposed method can achieve state-of-the-art objective and
subjective quality results. Specifically, HDCFM achieves a PSNR gain of 0.81 dB
at a parameter of about 100K. The number of parameters is 1/14th of the
previous state-of-the-art methods. The test code will be released soon.
