Image compression with upsampling encodes information to succeedingly increase image resolution, for example by encoding differences in FUIF and JPEG XL. It is useful for progressive decoding, also often can improve compression ratio - both for lossless compression and e.g. DC coefficients of lossy. However, the currently used solutions rather do not exploit context dependence for encoding of such upscaling information. This article discusses simple inexpensive general techniques for this purpose, which allowed to save on average $0.645$ bits/difference (between $0.138$ and $1.489$) for the last upscaling for 48 standard $512\times 512$ grayscale 8 bit images - compared to assumption of fixed Laplace distribution. Using least squares linear regression of context to predict center of Laplace distribution gave on average $0.393$ bits/difference savings. The remaining savings were obtained by additionally predicting width of this Laplace distribution, also using just the least squares linear regression.
For RGB images, optimization of color transform alone gave mean $\approx 4.6\%$ size reduction comparing to standard YCrCb if using fixed transform, $\approx 6.3\%$ if optimizing transform individually for each image. Then further mean $\approx 10\%$ reduction was obtained if predicting Laplace parameters based on context. The presented simple inexpensive general methodology can be also used for different types of data like DCT coefficients in lossy image compression.
Ultra high resolution (UHR) images are almost always downsampled to fit small displays of mobile end devices and upsampled to its original resolution when exhibited on very high-resolution displays. This observation motivates us on jointly optimizing operation pairs of downsampling and upsampling that are spatially adaptive to image contents for maximal rate-distortion performance. In this paper, we propose an adaptive downsampled dual-layer (ADDL) image compression system. In the ADDL compression system, an image is reduced in resolution by learned content-adaptive downsampling kernels and co…
Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement
Hangzhou Dianzi University（杭州电子科技大学）；State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）；Xinhua Zhiyun Technology Co., Ltd.（新华智云科技有限公司）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Netflix Inc.（奈飞公司）
We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our approach is simple: compose a pair of differentiable downsampling/upsampling layers that sandwich a neural compression model. To determine resize factors for different inputs, we utilize another neural network jointly trained with the compression model, with the end goal of minimizing the rate-distortion objective. Our results suggest that "compression friendly" downsampled representations can be quickly determined during encoding by using an auxili…
Data compression often subtracts prediction and encodes the difference (residue) e.g. assuming Laplace distribution, for example for images, videos, audio, or numerical data. Its performance is strongly dependent on the proper choice of width (scale parameter) of this parametric distribution, can be improved if optimizing it based on local situation like context. For example in popular LOCO-I \cite{loco} (JPEG-LS) lossless image compressor there is used 3 dimensional context quantized into 365 discrete possibilities treated independently. This article discusses inexpensive approaches for explo…
To achieve higher accuracy in machine learning tasks, very deep convolutional neural networks (CNNs) are designed recently. However, the large memory access of deep CNNs will lead to high power consumption. A variety of hardware-friendly compression methods have been proposed to reduce the data transfer bandwidth by exploiting the sparsity of feature maps. Most of them focus on designing a specialized encoding format to increase the compression ratio. Differently, we observe and exploit the sparsity distinction between activations in earlier and later layers to improve the compression ratio. W…
In this paper, we propose an image compression algorithm called Microshift. We employ an algorithm hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, then the sub-quantized values are further compressed. Two methods, the FAST and MRF model, are proposed to recover the bit-depth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. Our compression algorithm compresses images to 1.25 bits per pixel on average with PSNR of 33.16 dB, outpe…
Waseda University（早稻田大学）；Waseda Research Institute for Science and Engineering（早稻田大学科学与工程研究院）；Shanghai Jiao Tong University（上海交通大学）
Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages. Exploiting both advantages is a point worth exploring, which has two challenges: 1) how to effectively fuse the two methods? 2) how to achieve higher performance with a suitable complexity? In this paper, we propose an efficient parallel Transformer-CNN Mixture (TCM) block with a controllable compl…
To enrich the functionalities of traditional cameras, light field cameras record both the intensity and direction of light rays, so that images can be rendered with user-defined camera parameters via computations. The added capability and flexibility are gained at the cost of gathering typically more than $100\times$ greater amount of information than conventional images. To cope with this issue, several light field compression schemes have been introduced. However, their ways of exploiting correlations of multidimensional light field data are complex and are hence not suited for inexpensive l…
Based on point spread function (PSF) engineering and astigmatism due to a pair of cylindrical lenses, a novel compressed imaging mechanism is proposed to achieve single-shot incoherent 3D imaging. The speckle-like PSF of the imaging system is sensitive to axial shift, which makes it feasible to reconstruct a 3D image by solving an optimization problem with sparsity constraint. With the experimentally calibrated PSFs, the proposed method is demonstrated by a synthetic 3D point object and real 3D object, and the images in different axial slices can be reconstructed faithfully. Moreover, 3D multi…
A Survey on Medical Image Compression: From Traditional to Learning-Based Approaches
Beijing Jiaotong University（北京交通大学）；Tsinghua University（清华大学）；Shanghai United Imaging Healthcare Co., Ltd（上海联影医疗科技股份有限公司）；City University of Hong Kong（香港城市大学）
Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality. Unfortunately, existing quality enhancement approaches for compressed images neglect the inherent characteristic of defocus, resulting in inferior performance. This paper finds that in compressed images, significantly defocused regions have better compression quality, and two regions with different defocus values possess diverse texture patterns. These observations motivate our defocus-aware quality enhancement (DAQE) approach. Specifically, …
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Centre for Robotics and Automation, City University of Hong Kong（香港城市大学机器人与自动化中心）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）
