The human visual system (HVS) exhibits nonlinear sensitivity to the distortions introduced by lossy image and video coding. This effect is due to the luminance masking, contrast masking, and spatial and temporal frequency masking characteristics of the HVS. This paper proposes a novel perception-based quantization to remove nonvisible information in high dynamic range (HDR) color pixels by exploiting luminance masking so that the performance of the High Efficiency Video Coding (HEVC) standard is improved for HDR content. A profile scaling based on a tone-mapping curve computed for each HDR frame is introduced. The quantization step is then perceptually tuned on a transform unit basis. The proposed method has been integrated into the HEVC reference model for the HEVC range extensions (HM-RExt), and its performance was assessed by measuring the bitrate reduction against the HM-RExt. The results indicate that the proposed method achieves significant bitrate savings, up to 42.2%, with an average of 12.8%, compared with HEVC at the same quality (based on HDR-visible difference predictor-2 and subjective evaluations).

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High Dynamic Range Video Compression Exploiting Luminance Masking

Ultra-high definition (UHD) is currently being deployed in video distribution pipelines for improved quality of experience. This new recommendation not only improves spatial and temporal resolution, but also the quality and accuracy of pixels using wide color gamut (WCG) and high dynamic range (HDR) technologies. Indeed, HDR video technology aims at conveying the full range of perceptible shadow and highlight details with enough different tonal levels to prevent loss of visual information, while WCG increases the amount of visible color that can be represented. However, these technologies require different standard digital pixel representations than the ones currently employed in high definition (HD) television. While the impact of increasing spatial and temporal resolution (i.e., increase of resolution and frame rate) on compression efficiency has already been assessed, it is not clear how new pixel representations would affect the compression performance. In this paper, we discuss the differences between HD color pixel representation and the newly standardized representations developed to meet the requirements introduced by WCG and HDR. Moreover we explain why existing HD pixel representations are sub-efficient for encoding HDR and WCG pixels. We also perform a statistical analysis of pixel distribution in real images to explain how pixel’s representation influences compression efficiency. Results show that by tailoring a pixel representation upon the range of luminance and color values accessible in content and displays, bandwidth savings can be achieved while increasing the quality of delivered content. We conclude this paper by discussing the shortcomings of the color pixel representation recommended for the UHD television standards and provide guidelines to create a more efficient one.

Compression Efficiency of High Dynamic Range and Wide Color Gamut Pixel’s Representation

High Dynamic Range (HDR) technology is able to offer high levels of immersion with a dynamic range comparable to the Human Visual System (HVS). A primary drawback of HDR is that its memory and bandwidth requirements are significantly higher than for conventional video. The challenge is thus to develop means for efficiently compressing the video to a manageable bitrate without compromising perceptual quality. In this paper, we propose an HDR compression method based on an optimized bit-depth transformation, and HVS model based wavelet transform denoising. Experimental results indicate that the proposed method outperforms previous approaches and operates in accordance with characteristics of the HVS, tested objectively using a Visible Difference Predictor (VDP).

Perception-based high dynamic range video compression with optimal bit-depth transformation

The invention relates to a method and device for quantising the floating value of a pixel of an image by rounding either to a lesser whole number, to a greater whole number, or to the whole number closest to this floating value. The method is characterised in that the selection of rounding this floating value is determined based on a test value.

Method and device for quantising the floating value of a pixel in an image

High dynamic range (HDR) technology is able to offer high levels of immersion with a dynamic range comparable to that of the human visual system. This increase in the level of visual immersion comes at the cost of higher bit rate requirements compared with those associated with conventional imaging technologies. As a result, efficient HDR-specific coding solutions are necessary. In this chapter, we review existing HDR image and video coding methods, emphasizing how these exploit certain properties of the human visual system and how they provide enhanced rate-quality performance compared with conventional approaches.

High dynamic range video compression

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.

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Fabrice LeLéannec

Papers

Prediction-guided quantization for video tone mapping