Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-23/issue-2/029901/Color-Appearance-Models/10.1117/1.JEI.23.2.029901.pdf

Color Appearance Models

IEEE International Conference on Image Processing

There is disclosed a method, an apparatus, a server, a client and a non-transitory computer readable medium comprising a computer program stored therein for motion compensated video coding and decoding. Texture block motion information is used to derive disparity/depth motion information. Alternatively, disparity/depth motion information is used to derive texture block motion information.

An apparatus, a method and a computer program for video coding and decoding

With the advancement in modern video processing technologies, the requirement of an efficient coding algorithm for compressing the video data in a huge surveillance system is necessary. The video data rate in a surveillance system plays a crucial role in determining the performance of the compression algorithm. In this paper, a novel compression algorithm for effectively compressing the video inputs from the surveillance systems is presented. The traditional methods fail to eliminate the redundancies in the video input, and can’t meet the current requirement standards in the modern technologies. This results in the increased storage requirement for input video and also makes it time-consuming in processing the video input in real time. In order to overcome the above issues, the proposed algorithm made sufficient modification in the traditional run length coding algorithm by encoding the frames and removing the redundancies using the texture information similarity in the surveillance video, thereby achieved a better compression rate of 50% for a huge dataset of surveillance videos when compared to existing methodologies.

/pdf/an-enhanced-coding-algorithm-for-efficient-video-coding-40m9gkd2ff.pdf

An Enhanced Coding Algorithm for Efficient Video Coding

EML-NET:An Expandable Multi-Layer NETwork for Saliency Prediction

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

In this paper, we proposed a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map. We first investigate the effect of depth of CNNs for NR-IQA by comparing our proposed ten-layer Deep CNN (DCNN) for NR-IQA with the state-of-the-art CNN architecture proposed by Kang et al. (2014). Our results show that the DCNN architecture can deliver a higher accuracy on the LIVE dataset. To mimic human vision, we introduce saliency maps combining with CNN to propose a Saliency-based DCNN (SDCNN) framework for NR-IQA. We compute a saliency map for each image and both the map and the image are split into small patches. Each image patch is assigned with a patch importance value based on its saliency patch. A set of Salient Image Patches (SIPs) are selected according to their saliency and we only apply the model on those SIPs to predict the quality score for the whole image. Our experimental results show that the SDCNN framework is superior to other state-of-the-art approaches on the widely used LIVE dataset. The TID2008 and the CISQ image quality datasets are utilised to report cross-dataset results. The results indicate that our proposed SDCNN can generalise well on other datasets.

https://link.springer.com/content/pdf/10.1007%2Fs11042-017-5070-6.pdf

Saliency-based deep convolutional neural network for no-reference image quality assessment

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).

/pdf/high-dynamic-range-video-compression-exploiting-luminance-fkyhl04ef6.pdf

High Dynamic Range Video Compression Exploiting Luminance Masking

In this paper we propose a No-Reference Image Quality Assessment (NR-IQA) method on High Dynamic Range (HDR) images by combining deep Convolutional Neural Networks (CNNs) with saliency maps. The proposed method utilises the power of deep CNN architectures to extract quality features which can be applied cross HDR and Standard Dynamic Range (SDR) domains. To introduce human visual system to CNNs, a saliency map algorithm is used to select a subset of salient image patches to evaluate on. Our CNN-based method delivers a state-of-the-art performance in HDR NR-IQA experiment, competitive with full reference IQA methods.

/pdf/blind-high-dynamic-range-image-quality-assessment-using-deep-2qxxlr669u.pdf

Blind high dynamic range image quality assessment using deep learning

High Dynamic Range (HDR) imaging technologies can provide high levels of immersion through a dynamic range that meets and even exceeds the instantaneous range of the Human Visual System (HVS). This increase in the level of immersion comes at the cost of significantly higher bit-rate requirements compared to those associated with conventional imaging technologies. As a result, efficient HDR-relevant coding solutions have to be developed. In this paper, we present a review of existing HDR image and video coding methods, thus establishing the current state of the art. We additionally present some of our own recent results in this area.

Yang Zhang

Papers

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

Saliency-based deep convolutional neural network for no-reference image quality assessment

High Dynamic Range Video Compression Exploiting Luminance Masking

Blind high dynamic range image quality assessment using deep learning

High Dynamic Range image & video compression a review