Progressive Image Compression Research Articles

Progressive image compression for Gaussian mixture model quartile intervals

Progressive image compression for Gaussian mixture model quartile intervals

Learned Progressive Image Compression With Dead-Zone Quantizers

Progressive coding is essential to the practical deployment of learned image compression over heterogeneous networks and clients. Existing methods for learned progressive image compression require complex and empirical design to achieve near-optimal rate-distortion performance over a wide range of bit-rates. However, these methods are limited by the implicit learned mechanism based on neural networks and introduction of uniform quantizers. In this paper, we propose generalized learned progressive image compression with analytic rate-distortion optimization using dead-zone quantizers on the latent representation. Specifically, we reveal that dead-zone quantizers, as a general case of uniform quantizers, are equivalent to uniform quantizers in fixed-rate nonlinear transform coding and can prevent extra redundancy in embedded quantization for progressive coding. Consequently, we propose rate-distortion optimized learned progressive coding by approximating the optimal quantizer in the source spaces using dead-zone quantizers in an analytic manner on the Laplacian source. To our best knowledge, this paper is the first to achieve general learned progressive coding from the perspective of optimal quantizers. The proposed method achieves theoretically sound and practically efficient embedded quantization and learned progressive coding of latent representations with improved rate-distortion performance. It can also enable embedded quantization with diverse assignments of truncation points and support flexible configuration of quality layers of varying numbers and at varying target bit-rates. Furthermore, we successfully incorporate the proposed method into existing pre-trained fixed-rate models to realize progressive learned image compression without re-training. Experimental results demonstrate that the proposed method achieves state-of-the-art rate-distortion performance in learned progressive image compression compared with traditional codecs and recent learned methods.

Progressive Deep Image Compression for Hybrid Contexts of Image Classification and Reconstruction

Progressive deep image compression (DIC) with hybrid contexts is an under-investigated problem that aims to jointly maximize the utility of a compressed image for multiple contexts or tasks under variable rates. In this paper, we consider the contexts of image reconstruction and classification. We propose a DIC framework, called residual-enhanced mask-based progressive generative coding (RMPGC), designed for explicit control of the performance within the rate-distortion-classification-perception (RDCP) trade-off. Three independent mechanisms are introduced to yield a semantically structured latent representation that can support parameterized control of rate and context adaptation. Experimental results show that the proposed RMPGC outperforms a benchmark DIC scheme using the same generative adversarial nets (GANs) backbone in all six metrics related to classification, distortion, and perception. Moreover, RMPGC is a flexible framework that can be applied to different neural network backbones. Some typical implementations are given and shown to outperform the classic BPG codec and four state-of-the-art DIC schemes in classification and perception metrics, with a slight degradation in distortion metrics. Our proposal of a nonlinear-neural-coded and richly structured latent space makes the proposed DIC scheme well suited for image compression in wireless communications, multi-user broadcasting, and multi-tasking applications.

Underwater radio frequency image sensor using progressive image compression and region of interest

The increasing demand for underwater robotic intervention systems around the world in several application domains requires more versatile and inexpensive systems. By using a wireless communication system, supervised semi-autonomous robots have freedom of movement; however, the limited and varying bandwidth of underwater radio frequency (RF) channels is a major obstacle for the operator to get camera feedback and supervise the intervention. This paper proposes the use of progressive (embedded) image compression and region of interest (ROI) for the design of an underwater image sensor to be installed in an autonomous underwater vehicle, specially when there are constraints on the available bandwidth, allowing a more agile data exchange between the vehicle and a human operator supervising the underwater intervention. The operator can dynamically decide the size, quality, frame rate, or resolution of the received images so that the available bandwidth is utilized to its fullest potential and with the required minimum latency. The paper focuses first on the description of the system, which uses a camera, an embedded Linux system, and an RF emitter installed in an OpenROV housing cylinder. The RF receiver is connected to a computer on the user side, which controls the camera monitoring parameters, including the compression inputs, such as region of interest (ROI), size of the image, and frame rate. The paper focuses on the compression subsystem and does not attempt to improve the communications physical media for better underwater RF links. Instead, it proposes a unified system that uses well-integrated modules (compression and transmission) to provide the scientific community with a higher-level protocol for image compression and transmission in sub-sea robotic interventions.

Methods and algorithms for compact representation of graphic information in computer systems

Considering that a large amount of information, that transmitted in digital communication systems, accounts for graphic information, the development and improvement of methods and algorithms for compact representation of image data are very topical task.This paper shows the results of the study process of compact presentation of graphic information in computer systems, namely:− the basic methods of compact presentation of graphic information are considered. There are Gray reflex codes, progressive image compression, intuitive methods, JPEG, JPEG-LS, wavelet techniques, a mathematical transformation of the image;− the main indicators of algorithms for compact presentation of graphic information are considered. There are RLE, LZW, Huffman algorithm, JBIG, JPEG, Lossless JPEG, fractal algorithm, recursive algorithm, JPEG 2000;− advantages and disadvantages of methods and algorithms are revealed;− system analysis of opportunities for basic methods and algorithms for compression of graphical information is given.Investigated in the article impact of using combinations of methods of compact presentation of graphic information in computer systems at the results of the major compression algorithms will identify further ways to improve the degree of compression of graphic information.

Research on Image Technology with Progressive Image Compression Based on JPEG and Laplacian Pyramid

Progressive image transmission is a kind of image technology has been widely used in various fields, it can not only save bandwidth but also improve the user experience to meet user demand for different image quality. According to user's demand for image quality, realizing the progress of image compression coding flow can meet the demand of users. This article mainly introduce by means of JPEG and Laplacian pyramid coding principle implement progressive image compression.

Adaptive, scalable and robust watermarking for wavelet-based progressive image transmission

Image watermarking refers to the process of embedding an authentication message, called watermark, into the host image to uniquely identify the ownership. In this paper, an adaptive, scalable, blind and robust wavelet-based watermarking approach is proposed. The proposed approach enables scalable watermark detection and provides robustness against progressive wavelet image compression. A multiresolution decomposition of the binary watermark is inserted into the selected coefficients of the wavelet-decomposed image that represent the high activity regions of the image. The watermark insertion is started from the lowest frequency sub-band of the decomposed image and each decomposed watermark sub-band is inserted into its counterpart sub-band of the decomposed image. In the lowest frequency sub-band, coefficients with maximum local variance and in the higher frequency sub-bands, coefficients with maximum magnitude are selected. The watermarked test images are transparent according to the human vision system characteristics and do not show any perceptual degradation. The experimental results very efficiently prove the robustness of the approach against progressive wavelet image coding even at very low bit rates. The watermark extraction process is completely blind and multiple spatial resolutions of the watermark are progressively detectable from the compressed watermarked image. This approach is a suitable candidate for providing efficient authentication for progressive image transmission applications especially over heterogeneous networks, such as the Internet.

Hybrid discrete cosine transform–discrete wavelet transform for progressive image compression

In this paper, we propose an image compression algorithm that uses a hybrid transform and an improved modified set partitioning in hierarchical trees (SPIHT) coding algorithm. The proposed transform uses the subband discrete cosine transform to decompose the image into multiresolution subbands where the discrete wavelet transform is then used to code the low frequencies. Then, we use the SPIHT coding method to code the transformed coefficients. For the SPIHT algorithm, we have proposed a method to reduce the distortion introduced by the SPIHT technique between the original and reconstructed images. The obtained results show the efficiency of the proposed hybrid method in terms of peak signal-to-noise ratio and visual quality.

Finger Print Image Compression for Extracting Texture Features and Reconstructing

A progressive fingerprint image compression using ridge detection is performed. The image is decomposed into two components. The first component is the primary component, which contains the ridges and bifurcations; the other component is the secondary component, which contains the textures and the features. The decoding process is based on reconstructing a hybrid surface [1] with the means of ridges. The encoding scheme is performed using the tracing algorithm .The proposed method gives a high compression ratio 50:1 which is higher than the fbi specified. The method is also helpful at recognition as the minutia can be driven directly from the compressed image so decompression is not required.

Stylized multiresolution image representation

We integrate stylized rendering with an efficient multiresolution image representation, enabling a user to control how compression affects the aesthetic appearance of an image. We adopt a point-based rendering approach to progressive image transmission and compression. We use a novel, adaptive farthest point sampling algorithm to represent the image at progressive levels of detail, balancing global coverage with local precision. A progressively generated discrete Voronoi diagram forms the common foundation for our sampling and rendering framework. This framework allows us to extend traditional photorealistic methods of image reconstruction by scattered data interpolation to encompass nonphotorealistic rendering. It supports a wide variety of artistic rendering styles based on geometric subdivision or parametric procedural textures. Genetic programming enables the user to create original rendering styles through interactive evolution by aesthetic selection. We compare our results with conventional compression, and we discuss the implications of using nonphotorealistic representations for highly compressed imagery.

Still Image Compression with Mean-Residual Domain Adaptive Resolution Vector Quantization Technique

Abstract With the glowing importance of an advanced image processing methods for digital image, an efficient compression algorithm is increasingly important. In this paper we present progressive image compression algorithm based on Vector Quantization (VQ). Mean-Residual domain Adaptive Resolution Vector Quantization technique (MAR-VQ) that combines the adaptive resolution method with the mean-residual method has been developed. The adaptive resolution method changes the resolution of image adaptively according to the form of a pixel block texture in order to increase the performance of compound image compression. In addition, the mean-residual method was introduced as one of the additional techniques to increase continuous-tone image quality by VQ. As a result, MAR-VQ can realize much superior compression performance than the worldwide standard JPEG 2000.

A Progressive Image Compression Method Based on EZW Algorithm

A simple method based on the EZW algorithm is presented for improving image compression performance. Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's EZW(Embedded Zerotree Wavelets)(1), Said and Pearlman's SPIHT(Set Partitioning In Hierarchical Trees)(2), and Bing-Bing Chai's SLCCA(Significance-Linked Connected Component Analysis for Wavelet Image Coding)(3). The EZW algorithm is based on five key concepts: (1) a DWT(Discrete Wavelet Transform) or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, (4) universal lossless data compression which is achieved via adaptive arithmetic coding. and (5) DWT coefficients' degeneration from high scale subbands to low scale subbands. In this paper, we have improved the self-similarity statistical characteristic in concept (5) and present a progressive image compression method.

Progressive Medical Image Transmission and Compression

Digital radiology places very high demands on the networking and digital storage infrastructure of hospitals. In addition to having quite stringent requirements on the quality of the images displayed to the radiologist, much of the technical challenge resides in the necessity of displaying desired images as rapidly as possible. We present an infrastructure for progressive transmission and compression of medical images, which can refine an initial image by increasing the detail information not only in scale-space, but also in coefficient precision. The approach is based on the embedded zerotree wavelet (EZW) algorithm. This algorithm offers a tremendous amount of flexibility in meeting the bandwidth and image quality constraints in a radiological imaging environment. We propose an application of the EZW algorithm in progressive medical image transmission in which it can specify and control both the resolution constraint and rate constraint. The presented method can provide a framework for lossy image compression, with performance far superior to those provided by the standard JPEG algorithm. Also due to the flexibility of the method we will show how any region of interest of an image can be sent progressively.

Preserving step edges in low bit rate progressive image compression

With the growing importance of low-bandwidth applications, such as wireless access to the Internet, images are often sent or received at low bit rates. At these bit rates, they suffer from significant distortion and artifacts, making it difficult for those viewing the images to understand them. We present two progressive compression algorithms that focus on preserving the clarity of important image features, such as edges, at compression ratios of 80:1 and more. Both algorithms capture and encode the locations of important edges in the images. The first algorithm then transmits a standard SPIHT (set partitioning in hierarchical trees) bit stream, and at the decoder applies a nonlinear edge-enhancement procedure to improve the clarity of the encoded edges. The second approach uses a modified wavelet transform to "remove" the edges, and encodes the remaining texture information using SPIHT. With both approaches, features in the images that may be important for recognition are well preserved, even at low bit rates.

Progressive edge detection compression for fingerprint images

AbstractIn this article, a progressive fingerprint image compression (for storage or transmission) that uses an edge detection scheme is developed. First, the image is decomposed into two components: the primary component, which contains the edges, and the secondary component, which contains the textures and the features. Then, a general grasp for the image is reconstructed in the first stage at bit rates of 0.0223 and 0.0245 bpp for the tested fingerprints images (samples 1 and 2), respectively. The quality of the reconstructed images is competitive with the 0.75‐bpp target bit set by the FBI standard. Also, the compression ratio and the image quality of this algorithm are competitive with other methods reported in the literature. The compression ratio for our algorithm is about 45:1 (0.180 bpp). © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 211–216, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10025

Fast variable run-length coding for embedded progressive wavelet-based image compression

Run-length coding has attracted much attention in wavelet-based image compression because of its simplicity and potentially low complexity. The main drawback is the inferior RD-performance compared to the state-of-the-art-coder SPIHT. In this paper, we concentrate on the embedded progressive run-length code of Tian and Wells (1996, 1998). We consider significance sequences drawn from the scan in the dominant pass. It turns out that self-similar curves for scanning the dominant pass increase the compression efficiency significantly. This is a consequence of the correlation of direct neighbors in the wavelet domain. This dependence can be better exploited by using groups of coefficients, similar to the SPIHT algorithm. This results in a new and very fast coding algorithm, which shows performance similar to the state-of-the-art coder SPIHT, but with lower complexity and small and fixed memory overhead.

Restart marker regulation technique for progressive JPEG image coding in mobile communications

In this letter, an error-robust and JPEG compliant progressive image compression scheme over wireless channels is presented. The use of restart markers in the JPEG standard provides the resynchronization function for error handling. Unfortunately, misinterpreted markers may cause serious error damage due to the error propagation. Therefore, a restart marker regulation technique is proposed here to preprocess restart markers at the decoding end. All erroneous restart markers are corrected and rearranged in the correct order. After decoding, isolated erroneous restart intervals are detected and further processed by the error concealment to reduce image degradation. The simulations demonstrate that the proposed scheme does significantly improve the image quality in error-prone environments.

Progressive image coding for noisy channels

We cascade an existing image coder with carefully chosen error control coding, and thus produce a progressive image compression scheme whose performance on a noisy channel is significantly better than that of previously known techniques. The main idea is to trade off the available transmission rate between source coding and channel coding in an efficient manner. This coding system is easy to implement and has acceptably low complexity. Furthermore, effectively no degradation due to channel noise can be detected; instead, the penalty paid due to channel noise is a reduction in source coding resolution. Detailed numerical comparisons are given that can serve as benchmarks for comparisons with future encoding schemes. For example, for the 512/spl times/512 Lena image, at a transmission rate of 1 b/pixel, and for binary symmetric channels with bit error probabilities 10/sup -3/, 10/sup -2/, and 10/sup -1/, the proposed system outperforms previously reported results by at least 2.6, 2.8, and 8.9 dB, respectively.

Progressive technique for human face image archiving and retrieval

A progressive region-based image compression technique is presented. This technique is based on the secondgeneration image compression paradigm, representing images as a union of homogeneous regions in harmony with the characteristics of the human visual system. The technique is progressive, allowing for an initial coarse version of the image to be reconstructed quickly from the compressed image data, and then refined through to the desired degree of fidelity. This paper describes how this technique can be applied to human face image archiving and retrieval. Compression results are provided and comparison with the JPEG standard is undertaken.

Methods in digital image processing for subsea applications

Some aspects in digital image processing have been involved in this paper. They are the suitable resolution of digital images for subsea applications, a few filters for noise reduction of image date, and a progressive image compression algorithm for image-based teleoperation over an ultrasonic link with very narrow bandwidth. In this paper, only grey-level images are taken into account, and real images are intensively used for testing.The researches presented in this paper are aimed at building a feasible real-time image processing and robot vision system for a subsea application. Although not complete, the analyses are useful elements.