Yannick Olivier

High dynamic range video distribution using existing video codecs

High Dynamic Range (HDR) is a strong candidate for the next video format, closer to the human perception for an increased user experience. However, the HDR video compression field is still under much investigation. Most of the time, HDR video codecs are based on already existing distribution-dedicated LDR video codecs, for instance H.264/AVC or H.265/HEVC. Some coding methods use two instances of a LDR codec: one for the LDR layer and one for the HDR residual layer. Other methods encode HDR frames directly using a high profile professional codec with a bit-depth well beyond the standard 8 bits. In this paper, we present a technique that requires only one instance of an 8-bit distribution codec. Our technique transforms HDR frames into LDR-like ones and uses metadatas to help the decoder to achieve a good HDR reconstruction. Results show that our solution performs well for bit-rate targeted in broadcast video content.

prediction-guided quantization for video tone mapping

Tone Mapping Operators (TMOs) compress High Dynamic Range (HDR) content to address Low Dynamic Range (LDR) displays. However, before reaching the end-user, this tone mapped content is usually compressed for broadcasting or storage purposes. Any TMO includes a quantization step to convert floating point values to integer ones. In this work, we propose to adapt this quantization, in the loop of an encoder, to reduce the entropy of the tone mapped video content. Our technique provides an appropriate quantization for each mode of both the Intra and Inter-prediction that is performed in the loop of a block-based encoder. The mode that minimizes a rate-distortion criterion uses its associated quantization to provide integer values for the rest of the encoding process. The method has been implemented in HEVC and was tested over two different scenarios: the compression of tone mapped LDR video content (using the HM10.0) and the compression of perceptually encoded HDR content (HM14.0). Results show an average bit-rate reduction under the same PSNR for all the sequences and TMO considered of 20.3% and 27.3% for tone mapped content and 2.4% and 2.7% for HDR content.

Perceptual video coding based on MB classification and rate–distortion optimization

Abstract The efficiency of a video coding scheme should be defined as the video quality achieved for a given bit-rate. The quality refers here to the subjective quality perceived by the final user. This is the only valid criteria, and it involves the user vision properties. Therefore, in order to optimize this perceived video quality, a new perceptual coding scheme is proposed taking into account the Human Visual System (HVS). Perceptual distortion measures are included in the encoding loop to compute an adaptive local quantization step. The idea is that optimizing the choice of macroblock quantization parameters based on a more accurate perceptual distortion measure would result in an improved subjective quality. The resulting macroblock-level rate allocation problem is first modeled as a constrained optimization problem solved with a Lagrangian multiplier-based algorithm. The local adaptation is performed by a classification of each macroblock according to its distortion–quantization properties. A learning strategy applied on a set of sequences provides a set of representative distortion–quantization curves for all macroblock types. Then a rate–quantization model is derived from the ρ - domain linear approximation to reach the target bit-rate. A two-pass only algorithm is required to compute the necessary models and encode each macroblock. The complexity of the proposed solution is thus reduced compared to exhaustive search algorithms. The SSIM (Structural SIMilarity) is used as in-loop distortion measure, and the performances are evaluated with another perceptual measure, more correlated to the user perception, called the WQA (Wavelet Quality Assessment). The coding schemes being based on a biological possible modeling of the Human Visual System, the video includes less visual artifacts and a more uniform subjective quality, compared to traditional rate–distortion coding.