Adaptive Quantization Matrices for HD and UHD Display Resolutions in Scalable HEVC

HEVC contains an option to enable custom quantization matrices, which are designed based on the Human Visual System and a 2D Contrast Sensitivity Function. Visual Display Units, capable of displaying video data at High Definition and Ultra HD display resolutions, are frequently utilized on a global scale. Video compression artifacts that are present due to high levels of quantization, which are typically inconspicuous in low display resolution environments, are clearly visible on HD and UHD video data and VDUs. The default QM technique in HEVC does not take into account the video data resolution, nor does it take into consideration the associated display resolution of a VDU to determine the appropriate levels of quantization required to reduce unwanted video compression artifacts. Based on this fact, we propose a novel, adaptive quantization matrix technique for the HEVC standard, including Scalable HEVC. Our technique, which is based on a refinement of the current HVS-CSF QM approach in HEVC, takes into consideration the display resolution of the target VDU for the purpose of minimizing video compression artifacts. In SHVC SHM 9.0, and compared with anchors, the proposed technique yields important quality and coding improvements for the Random Access configuration, with a maximum of 56.5% luma BD-Rate reductions in the enhancement layer. Furthermore, compared with the default QMs and the Sony QMs, our method yields encoding time reductions of 0.75% and 1.19%, respectively.Comment: Data Compression Conference 201

JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC

The JCT-VC standardized Screen Content Coding (SCC) extension in the HEVC HM RExt + SCM reference codec offers an impressive coding efficiency performance when compared with HM RExt alone; however, it is not significantly perceptually optimized. For instance, it does not include advanced HVS-based perceptual coding methods, such as JND-based spatiotemporal masking schemes. In this paper, we propose a novel JND-based perceptual video coding technique for HM RExt + SCM. The proposed method is designed to further improve the compression performance of HM RExt + SCM when applied to YCbCr 4:4:4 SC video data. In the proposed technique, luminance masking and chrominance masking are exploited to perceptually adjust the Quantization Step Size (QStep) at the Coding Block (CB) level. Compared with HM RExt 16.10 + SCM 8.0, the proposed method considerably reduces bitrates (Kbps), with a maximum reduction of 48.3%. In addition to this, the subjective evaluations reveal that SC-PAQ achieves visually lossless coding at very low bitrates.Comment: Preprint: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Visually lossless coding in HEVC : a high bit depth and 4:4:4 capable JND-based perceptual quantisation technique for HEVC

Due to the increasing prevalence of high bit depth and YCbCr 4:4:4 video data, it is desirable to develop a JND-based visually lossless coding technique which can account for high bit depth 4:4:4 data in addition to standard 8-bit precision chroma subsampled data. In this paper, we propose a Coding Block (CB)-level JND-based luma and chroma perceptual quantisation technique for HEVC named Pixel-PAQ. Pixel-PAQ exploits both luminance masking and chrominance masking to achieve JND-based visually lossless coding; the proposed method is compatible with high bit depth YCbCr 4:4:4 video data of any resolution. When applied to YCbCr 4:4:4 high bit depth video data, Pixel-PAQ can achieve vast bitrate reductions – of up to 75% (68.6% over four QP data points) – compared with a state-of-the-art luma-based JND method for HEVC named IDSQ. Moreover, the participants in the subjective evaluations confirm that visually lossless coding is successfully achieved by Pixel-PAQ (at a PSNR value of 28.04 dB in one test)

Visible light-based human visual system conceptual model

There is a widely held belief in the digital image and video processing community, which is as follows: the Human Visual System (HVS) is more sensitive to luminance (often confused with brightness) than photon energies (often confused with chromaticity and chrominance). Passages similar to the following occur with high frequency in the peer reviewed literature and academic text books: “the HVS is much more sensitive to brightness than colour” or “the HVS is much more sensitive to luma than chroma”. In this discussion paper, a Visible Light-Based Human Visual System (VL-HVS) conceptual model is discussed. The objectives of VL-HVS are as follows: 1. To facilitate a deeper theoretical reflection of the fundamental relationship between visible light, the manifestation of colour perception derived from visible light and the physiology of the perception of colour. That is, in terms of the physics of visible light, photobiology and the human subjective interpretation of visible light, it is appropriate to provide comprehensive background information in relation to the natural interactions between visible light, the retinal photoreceptors and the subsequent cortical processing of such. 2. To provide a more wholesome account with respect to colour information in digital image and video processing applications. 3. To recontextualise colour data in the RGB and YCbCr colour spaces, such that novel techniques in digital image and video processing — including quantisation and artifact reduction techniques — may be developed based on both luma and chroma information (not luma data only)

JNCD-based perceptual compression of RGB 4:4:4 image data

In contemporary lossy image coding applications, a desired aim is to decrease, as much as possible, bits per pixel without inducing perceptually conspicuous distortions in RGB image data. In this paper, we propose a novel color-based perceptual compression technique, named RGB-PAQ. RGB-PAQ is based on CIELAB Just Noticeable Color Difference (JNCD) and Human Visual System (HVS) spectral sensitivity. We utilize CIELAB JNCD and HVS spectral sensitivity modeling to separately adjust quantization levels at the Coding Block (CB) level. In essence, our method is designed to capitalize on the inability of the HVS to perceptually differentiate photons in very similar wavelength bands. In terms of application, the proposed technique can be used with RGB (4:4:4) image data of various bit depths and spatial resolutions including, for example, true color and deep color images in HD and Ultra HD resolutions. In the evaluations, we compare RGB-PAQ with a set of anchor methods; namely, HEVC, JPEG, JPEG 2000 and Google WebP. Compared with HEVC HM RExt, RGB-PAQ achieves up to 77.8% bits reductions. The subjective evaluations confirm that the compression artifacts induced by RGB-PAQ proved to be either imperceptible (MOS = 5) or near-imperceptible (MOS = 4) in the vast majority of cases

Frequency-dependent perceptual quantisation for visually lossless compression applications

The default quantisation algorithms in the state-of-the-art High Efficiency Video Coding (HEVC) standard, namely Uniform Reconstruction Quantisation (URQ) and Rate-Distortion Optimised Quantisation (RDOQ), do not take into account the perceptual relevance of individual transform coefficients. In this paper, a Frequency-Dependent Perceptual Quantisation (FDPQ) technique for HEVC is proposed. FDPQ exploits the well-established Modulation Transfer Function (MTF) characteristics of the linear transformation basis functions by taking into account the Euclidean distance of an AC transform coefficient from the DC coefficient. As such, in luma and chroma Cb and Cr Transform Blocks (TBs), FDPQ quantises more coarsely the least perceptually relevant transform coefficients (i.e., the high frequency AC coefficients). Conversely, FDPQ preserves the integrity of the DC coefficient and the very low frequency AC coefficients. Compared with RDOQ, which is the most widely used transform coefficient-level quantisation technique in video coding, FDPQ successfully achieves bitrate reductions of up to 41%. Furthermore, the subjective evaluations confirm that the FDPQ-coded video data is perceptually indistinguishable (i.e., visually lossless) from the raw video data for a given Quantisation Parameter (QP)