Lossless HDR Image Compression by Modulo Encoding

With advancement of technology, the image processing techniques play an important role. Image processing is required to improve the pictorial information for human perception and for autonomous machine applications. It finds application in various fields such as quality control, remote sensing, imaging science, etc. It also finds application in areas where efficient storage and transmission of images is necessary, Say for example; we want to store an image in the computer with less disk space. Also, in cases where we want to signal an image or video on a transmission medium with less bandwidth communication channel. Images are usually in the form of matrices and an uncompressed image uses huge number of bytes for storage. Image compression is used to reduce the size of the bitmap without compromising on the quality of an image. This enables to store large number of images in the same given amount of storage space. Image compression will also reduce the time taken to transmit or to download images from the internet. Many methods are available to compress an image file. The image compression methods which have gained popularity are the transform based coding methods like Discrete Cosine Transform (DCT), discrete wavelet Transform (DWT) and fractals. However these methods have drawbacks like low compression ratio and high encoding time. In this paper a new hybrid methodology is proposed by combining lossy and lossless compression methods. The proposed hybrid technique combines DCT and fractal quadtree decomposition with Huffman encoding of threshold value 0.2. The results obtained from the proposed method are then compared with state-of-the-art compression methods.

HDR video quality assessment: Perceptual evaluation of compressed HDR video

Recently, visual representations using high dynamic range (HDR) images become increasingly popular, with advancement of technologies for increasing the dynamic range of image. HDR image is expected to be used in wide-ranging applications such as digital cinema, digital photography and next generation broadcast, because of its high quality and its powerful expression ability. HDR imaging technologies will spread its sphere of influence in imaging industry. In this paper, we review the state-of-the-art studies and the trends of the HDR imaging, in terms of the following three points: (1) HDR imaging sensor and HDR image generation techniques as image acquisition technologies, (2) encode method of HDR images for efficient transmission and storage, (3) human visual system issues associated with reproduction of HDR image.

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.

In this paper, a method for lossless and near lossless compression of large digital mammograms is proposed. This method is based on a predictive coder that uses integer-to-integer operations, and generates a two-layer embedded bit stream (i.e. near-lossless and refinement layer). The maximum tolerated pixel distortion is guaranteed in the near lossless component. Comparisons with other proposed approaches are based on a database of high-resolution 12 bits/pixel digital mammograms. The simulation results indicate that our lossless compression method can offer average bit rates 39, 5%, 24%, 7% and 2.5% better than PNG, JPEG 2000, JPEG-lossless, and LOCO, respectively. Besides, for the same images, the proposed method offers near lossless compression at relatively low computational cost, providing an average bit rate of 2.57 bits/pixel and PSNR of 47.77 dB.

Feature Point (FP) detection is a fundamental step in computer vision tasks. Although FP detectors are mostly designed to support Low Dynamic Range (LDR) images as input, interest in High Dynamic Range (HDR) images has increased recently due to their higher precision to register overexposed and underexposed areas in an image. As the detection of FPs is strongly dependent on the illumination of the scene, HDR images have the potential to be more robust than LDR images during FP detection. Known FP detectors, however, do not use the full potential of HDR images. In addition, few works have evaluated the performance of HDR images in this context. In this paper, we propose a modification of FP detectors aiming to improve FP detection on HDR images. To this end, we design a local mask based on the Coefficient of Variation (CV) of sets of pixels, creating thus a new step in FP detection. We compare our approach with popular FP detection methods using a standard evaluation metric, Repeatability Rate (RR) of FPs, and also Uniformity as a proposed new criterion. A dataset of images from scenes affected by camera transformations and substantial illumination changes was used as input. Experimental results show that our proposed algorithms give better Uniformity and RR in most HDR images from the dataset when compared to standard FP detectors. Moreover, they indicate that HDR images present a great potential to be explored in applications that rely on FP detection.

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

The new generation of organic light emitting diode display is designed to enable the high dynamic range (HDR), going beyond the standard dynamic range (SDR) supported by the traditional display devices. However, a large quantity of videos are still of SDR format. Further, most pre-existing videos are compressed at varying degrees for minimizing the storage and traffic flow demands. To enable movie-going experience on new generation devices, converting the compressed SDR videos to the HDR format (i.e., compressed-SDR to HDR conversion) is in great demands. The key challenge with this new problem is how to solve the intrinsic many-to-many mapping issue. However, without constraining the solution space or simply imitating the inverse camera imaging pipeline in stages, existing SDR-to-HDR methods can not formulate the HDR video generation process explicitly. Besides, they ignore the fact that videos are often compressed. To address these challenges, in this work we propose a novel imaging knowledge-inspired parallel networks (termed as KPNet) for compressed-SDR to HDR (CSDR-to-HDR) video reconstruction. KPNet has two key designs: Knowledge-Inspired Block (KIB) and Information Fusion Module (IFM). Concretely, mathematically formulated using some priors with compressed videos, our conversion from a CSDR-to-HDR video reconstruction is conceptually divided into four synergistic parts: reducing compression artifacts, recovering missing details, adjusting imaging parameters, and reducing image noise. We approximate this process by a compact KIB. To capture richer details, we learn HDR representations with a set of KIBs connected in parallel and fused with the IFM. Extensive evaluations show that our KPNet achieves superior performance over the state-of-the-art methods.

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y) , to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance. The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

Over the last decade, much research experience has been gained in the realm of high dynamic range imaging (HDRI). On account of the significantly improved visual experience that HDRI offers, industry has recently taken a strong interest in this set of technologies. This is evident in the active consideration of HDR by standardization bodies, for instance DVD and EBU in the context of UHDTV, and the emergence of demos at tradeshows. To enable a more engaging/immersive viewing experience in the home or in the cinema, a range of technologies, workflows and processes should be adapted, and HDRI would need to evolve to become HDRV: high dynamic range video. There exists a good number of solutions that each solves part of the problem. However, as a significant number of problems remain open, there is, up to now, no integrated pipeline that includes everything from capture to post-production, archival/storage, compression, transmission, and display. In this paper, we review the requirements for building high dynamic range video pipelines, and discuss how and where HDR components can and should be inserted. This leads to a range of different scenarios, each with their own expected quality levels, associated cost and required effort to implement in practice. In particular, we consider an ideal end-to-end HDR video chain, as well as several variants that involve either legacy content or conventional decoding and rendering devices.

Ghost detection and removal for high dynamic range images: Recent advances

Typical digital cameras exhibit a limitation regarding the dynamic range of the scene radiance they can capture. High Dynamic Range (HDR) imaging refers to methods and systems that aim to generate images that exhibit higher dynamic range between the lightest and the darkest parts of the an image. A typical approach for generating HDR images is exposure bracketing where multiple frames, each one with a different exposure setting, are captured and combined to a HDR image of the scene. The large number of images that exposure bracketing requires often leads to motion artefacts that limit the visual quality of the resulting HDR image. In this work, we propose a novel approach in HDR imaging that significantly reduces the necessary number of images. In our proposed system, we employ the notion of random exposure where each pixel of a single frame collects light for a random amount of time. By collecting a small number of such images, the full sequence of low dynamic range images can be reconstructed and subsequently used for HDR generation. The problem is solved by casting the reconstruction of the sequence as a nuclear norm minimization problem following the premises of the recently proposed theory of Matrix Completion. Experimental results suggest that the proposed method is able to reconstruct the sequence from as low as 20% of the images that traditional techniques require with minimal reduction in image quality.

This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.

Technique of high dynamic range imaging (HDRI) was introduced into conventional high dynamic range display (HDRD). Sharpness of image was further enhanced by improving local contrast ratio in the HDRI-based high dynamic range display.

Rate-distortion optimized layered coding of high dynamic range videos