High Quality Image Compression Research Articles

High-Quality Image Compression Algorithm Design Based on Unsupervised Learning.

Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizers are used for coding quantization, and importance maps are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently and realize the efficient processing of image compression.

Entropy encoder for low-power low-resources high-quality CFA image compression

An entropy encoder for high-quality image compression in low-power, low-resources devices like wireless capsule endoscopy (WCE) or wireless camera sensor network (WCSN) is proposed. The proposed entropy encoder is optimized for encoding data resulting from transformation and quantization of images captured with a color filter array (CFA) digital camera. It efficiently exploits existing correlations among color components in CFA images to improve the performance of the image compressor in terms of power consumption, silicon area, and compression ratio. The image compressor based on the new encoder requires only 22% of the memory size needed by the JPEG-LS-based CFA compressor (for an image with 512 columns). Despite this significant reduction in memory size, the average compression ratio in relation to the image quality is superior to other state-of-the-art algorithms of similar computational complexity and memory requirements. The image compressor based on the proposed encoder has been synthesized using a low-cost UMC 180 nm CMOS process as an intellectual property (IP) core. The design requires a very small silicon area (0.54 × 0.50 mm) and achieves excellent energy efficiency (30μJ per a single 512 × 512 image frame and 20 fps). The selected technology for the implementation is not only low-cost but also well suited to implement an RF transmitter in addition to the compressor to provide a cost effective System on Chip (SoC) for resource-constrained devices like WCE or WCSN.

Watercolor Image Processing Method for Big Data Analysis

The colors in a watercolor painting are transparent. The watercolor style uses one layer of color over another to achieve a special transparent visual effect. Therefore, the watercolor pixel color change level is more complex, and the color edge is not apparent. The traditional image processing method is challenging to carry out high-quality image transformation, image coding, image compression, and image segmentation. This paper proposes a watercolor image processing method oriented to big data analysis. First, the collected watercolor image is preprocessed to eliminate background noise in the watercolor image. Then, the watercolor image is colorized through image semantic segmentation. Based on the semantic segmentation results, the images can be clustered by different layers. These layers are then colorized according to their properties. Finally, the colored layers are combined with a degree of transparency to get the final watercolor-style image. The watercolor obtained by this method is more consistent with the actual watercolor painting form and fully uses the advantages of big data analysis. The computer simulation of the watercolor painting process and the parallel deep learning method can significantly improve the algorithm's efficiency.

High-Quality Color Image Compression by Quantization Crossing Color Spaces

Coding of a color image usually happens in the YCbCr space so that the rate-distortion optimization is conducted in this space. Due to the use of a non-unitary matrix in the RGB-to-YCbCr conversion, an optimal coding performance achieved in the YCbCr space does not guarantee an optimal quality in the RGB space, which would impact most display devices that need RGB signals as the inputs. In this paper, we first study the relationship between the coding distortions of the compressed RGB signals and the quantization errors occurred in the coded YCbCr signals. Then, we design a new quantization scheme crossing the RGB and YCbCr spaces to achieve a high-quality color image compression with the YCbCr 4:4:4 format. Although our proposed quantization takes place in the YCbCr space, it aims at reducing the coding distortion in the RGB space as much as possible. Experimental results demonstrate that our proposed method offers a significant quality gain over the existing block-based coding methods for various images.

Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression

Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms.

An End-to-End Compression Framework Based on Convolutional Neural Networks

Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high level vision applications such as recognition and understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with state-of-the-art deblocking or denoising post-processing methods.

Image compression algorithm of floral canopy based on mask hybrid coding for ROI

To achieve high-quality image compression of a floral canopy, a region of interest (ROI) mask of the wavelet domain was generated through the automatic identification of the canopy ROI and lifting the bit-plane of the ROI to obtain priority of coding for the ROI-set partitioning in hierarchical trees (ROI-SPIHT) coding. The embedded zerotree wavelet (EZW) coding was conducted for the background (BG) region of the image and a relatively more low-frequency wavelet coefficient was obtained using a relatively small amount of coding. Through the weighing factor r of the ROI coding amount, the proportion of the ROI and BG coding amount was dynamically adjusted to generate embedded, truncatable bit streams. Despite the location of truncation, the image information and ROI mask information required by the decoder can be guaranteed to achieve high-quality compression and reconstruction of the image ROI. The results indicated that under the same bit rate, the larger the r value is, the larger the peak-signal-to-noise ratio (PSNR) for the ROI reconstructed image and the smaller the PSNR for the BG reconstructed image. In the range of 0.07-1.09 bpp, the PSNR of the ROI reconstructed image was 42.65% higher on average than that of the BG reconstructed image, 43.95% higher on average than that of the composite image of the ROI and BG (ALL), and 16.84% higher on average than that of the standard SPIHT reconstructed image. Additionally, the mean square error of the quality evaluation index and similarity for the ROI reconstructed image were both better than those for the BG, ALL, and standard SPIHT reconstructed images. The texture distortion of the ALL image was smaller than that of the SPIHT reconstructed image, indicating that the image compression algorithm based on the mask hybrid coding for ROI (ROI-MHC) is capable of improving the reconstruction quality of an ROI image. When the weighing factor r is a fixed value, as the proportion of ROI (a) increases, the quality of ROI image reconstruction gradually decreases. Therefore, upon the application of the ROI-MHC image compression algorithm, high-quality reconstruction of the ROI image can be achieved through dynamically configuring r according to a. Under the same bit rate, the quality of the ROI-MHC image compression is higher than that of current compression algorithms of same classes and offers promising application opportunities. Keywords: floral canopy, ROI, mask hybrid coding, image compression, algorithm, wavelet transform DOI: 10.25165/j.ijabe.20171005.2772 Citation: Sun G X, Wang X C, Ding Y Q, Li Y H, Zhang B H, Li Y B, et al. Image compression algorithm of floral canopy based on mask hybrid coding for ROI. Int J Agric & Biol Eng, 2017; 10(5): 166–176.

Hybrid WPT‐BDCT transform for high‐quality image compression

The image compression performance of transform coders highly depends on the energy compaction (EC) capability of transforms. The popular transforms such as discrete cosine transform (DCT) and discrete wavelet transform (DWT) provide decent EC; however, their capability is not enough to deliver good quality image reconstruction for higher compression levels (CLs). In this study, the authors propose a new hybrid transform which is a fusion of wavelet packet transform (WPT) and block-DCT (BDCT) transform to achieve high-quality image compression. This new hybrid WPT-BDCT transform is able to attain higher EC than the existing transforms. Further, the authors have found a new energy compaction index (ECI) to evaluate the energy compactness of the image transforms. The proposed hybrid transform has been extensively evaluated, based on proposed ECI parameter, the visual quality assessment of reconstructed images and with the standard image quality indexes peak-signal-to-noise ratio and structural similarity index measure. It is reported that the proposed hybrid transform provides higher EC and outperforms the transforms namely DCT, DWT, WPT, multi-wavelet transform and existing hybrid transforms for all the CLs.

A WiSN node SoC with real-time image compressor and IEEE 802.15.4 MAC accelerator

This article presents a wireless image sensor node SoC (system-on-a-chip) for low-power wireless image sensor network (WiSN), in which camera chip interface, high-quality image compression and IEEE 802.15.4 compliant acceleration modules are integrated on chip. The proposed SoC contains a hardware-implemented real-time lossless JPEG (JPEG-LS) compression engine for Bayer Color Filter Arrays (Bayer CFA), reaching a 3.5 bits/pixel with peak signal to noise ratio (PSNR) greater than 46.3 dB and achieving a maximum 5 frames/s @16 MHz for VGA (640 × 480) colour images. The proposed hardware accelerator for IEEE 802.15.4 media access control (MAC) layer covers crucial protocol defined functions and algorithms, and reduces 45% software code in the host processor. This SoC has been fabricated in UMC 0.18 µm 1P6M CMOS process. The average power of the prototype chip is 18.2 mW at 3.0 V power supply and 16 MHz clock rate.

An Intelligent Method Based Medical Image Compression

Compression methods are important in many medical applications to ensure fast interactivity through large sets of images (e.g. volumetric data sets, image databases), for searching context dependant images and for quantitative analysis of measured data. Medical data are increasingly represented in digital form. The limitations in transmission bandwidth and storage space on one side and the growing size of image datasets on the other side has necessitated the need for efficient methods and tools for implementation. Many techniques for achieving data compression have been introduced. Wavelet transform techniques currently provide the most promising approach to high-quality image compression, which is essential for Teleradiology. This paper presents an approach of intelligent method to design a vector quantizer for image compression. The image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. The proposed approach presents an efficient method of vector quantization for image compression and application of SOFM.

Low-Complexity and High-Quality Image Compression Algorithm for Onboard Satellite

Compression reduces redundancy in data representation in order to achieve saving in the cost of storage and transmission. Image compression compensates for the limited on-board resources, in terms of mass memory and downlink and thus it provides a solution to the bandwidth vs. data volume dilemma of modern spacecraft. Thus compression is very important feature in payload image processing units of many satellites. A low complexity and high efficiency near-lossless image compression algorithm is suggested in this paper. The algorithm provides the average compression ratio of 1.403 with high image quality for lossless compression. Compression ratio increases as ∆ parameter increases. Using proposed algorithm compression ratio of 4.208 is achieved for near-lossless compression. The proposed algorithm has low memory cost suitable for hardware implementation.

An Adaptive Coding Pass Scanning Algorithm for Optimal Rate Control in Biomedical Images

High-efficiency, high-quality biomedical image compression is desirable especially for the telemedicine applications. This paper presents an adaptive coding pass scanning (ACPS) algorithm for optimal rate control. It can identify the significant portions of an image and discard insignificant ones as early as possible. As a result, waste of computational power and memory space can be avoided. We replace the benchmark algorithm known as postcompression rate distortion (PCRD) by ACPS. Experimental results show that ACPS is preferable to PCRD in terms of the rate distortion curve and computation time.

A NOVEL APPROACH FOR IMAGE COMPRESSION USING LIFTING TECHNIQUE AND TSVQ

The objective of image compression is to reduce the redundant amount of data and to achieve low bit rate without any apparent loss of image quality. In this paper, the compression process is achieved by using wavelet transform with lifting technique for decomposition and tree-structured vector scheme for quantization. Huffman coding method is used for encoding stage. Wavelet technique provides the most promising tool for high-quality image compression. Lifting transform provides a flexible tool for the construction of sub-band decomposition and perfect reconstruction. TSVQ has its advantages over conventional vector quantization by minimizing the computational complexity. Huffman coding is used for efficient coding of images at lower bit rate with minimal loss of information. The performance of the proposed work has been assessed with parameters like bits per pixel (bpp), compression ratio (CR), peak signal to noise ratio (PSNR), codebook size (CS), and mean squared error (MSE). The experimental results show that the proposed work yields a higher compression with better quality reconstructed image than the other conventional compression methods.

A WAVELET-BASED PERCEPTUAL IMAGE CODER INCORPORATING A NEW MODEL FOR COMPRESSION OF COLOR IMAGES

A wavelet-based perceptual image coder for the compression of color images is proposed here in which the coding structure is coupled with Human Visual System models to produce high quality images. The major contribution is the development of a new model for the compression of the color components based on psychovisual experiments, which quantifies the optimum amount of compression that can be applied to the color components for a given rate. The model is developed for YCbCr color space and the perceptually uniform CIE Lab color space. A complete coding structure for the compression of color images is developed by incorporating the new perceptual model. The performance of the proposed coder is compared with a wavelet-based coder that uses the quantization stage of the JPEG2000 standard. The perceptual quality of the compressed images is tested using the wavelet-based subjective and objective perceptual quality matrices such as Mean Opinion Score, Visual Information Fidelity and Visual Signal to Noise Ratio. Though the model is developed for a perceptually lossless high quality image compression, results obtained reveal that the proposed structure gives very good perceptual quality compared to the existing schemes for lower bit rates. These advantages make the proposed coder a candidate for replacing the encoder stage of the current image compression standards.

AN EFFICIENT DESIGN FOR MOTION-JPEG2000 SYSTEM IN REAL-TIME VIDEO ENCODING

Motion-JPEG2000 is derived from Part 1 of the JPEG2000 standard and provides high quality image compression in video coding system. This paper presents a real-time Motion-JPEG2000 encoding which is built on a fixed-point digital signal processor (DSP) as a single-chip implementation. The design addresses on the system-level design considerations. Among several modules in JPEG2000, Embedded Block Coding with Optimized Truncation (EBCOT) algorithm contains the highest computation complexity and also becomes the bottleneck of JPEG2000. Two speed-improved methods are proposed to significantly reduce the processing time of EBCOT. The fast algorithm is used by processing the three coding passes in sequential and is very suitable to the execution on programming-based processor. For Motion-JPEG2000 implementation, a real-time encoding system is built on the single-chip DSP processor without any extra hardware support. The optimization techniques are also applied to enhance the performance. Due to the contribution on algorithm and implementation issue, the complexity of Motion-JPEG2000 is largely reduced. Simulation results show that the proposed system can process 30 frames per second in CIF size (352 × 288 pixels) with 4:2:0 video sequences. For SDTV size resolution (760 × 480 pixels), the system can achieve near 20 frames per second.

Architecture design of full HD JPEG XR encoder for digital photography applications

To satisfy the high quality image compression requirement, the new JPEG XR compression standard is introduced. The analysis and architecture design with VLSI architecture of JPEG XR encoder are proposed in this paper which can encode 4:4:4 1920 times 1080 high definition photo in smooth. According to the simulation results, the throughput of the proposed design can encode 44.2 M samples/sec. This design can be used for digital photography applications to achieve low computation, low storage, and high dynamical range features.

High-Quality DCT-Based Image Compression Using Partition Schemes

This letter presents an advanced discrete cosine transform (DCT)-based image compression method that combines advantages of several approaches. First, an image is divided into blocks of different sizes by a rate-distortion-based modified horizontal-vertical partition scheme. Statistical redundancy of quantized DCT coefficients of each image block is reduced by a bit-plane dynamical arithmetical coding with a sophisticated context modeling. Finally, a post-filtering removes blocking artifacts in decompressed images. The proposed method provides significantly better compression than JPEG and other DCT-based techniques. Moreover, it outperforms JPEG2000 and other wavelet-based image coders

A Low-Complexity and High-Quality Image Compression Method for Digital Cameras

This letter proposes a new near-lossless image compression method with only one line buffer cost for a digital camera with Bayer format image. For such format data, it can provide a low average compression rate (4.24bits/pixel) with high-image quality (larger than 46.37dB where the error of every pixel is less than two). The experimental results show that the near-lossless compression method has better performance than JPEG-LS (lossless) with δ = 2 for a Bayer format image.

66.3: High Quality Image Compression System for Low Delay and Real-Time Wireless Transmission

We've developed a software model of image compression system supporting environments where both ordinary computer desktop images requiring interactive responsiveness and real-time motion picture images coexist. In comparison with MPEG-2, the system eliminates the delay and achieves higher image quality in many cases.

Medical image compression based on a morphological representation of wavelet coefficients.

Image compression is fundamental to the efficient and cost-effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high-quality image compression which is essential for diagnostic medical applications. A novel approach to image compression based on the wavelet decomposition has been developed which utilizes the shape or morphology of wavelet transform coefficients in the wavelet domain to isolate and retain significant coefficients corresponding to image structure and features. The remaining coefficients are further compressed using a combination of run-length and Huffman coding. The technique has been implemented and applied to full 16 bit medical image data for a range of compression ratios. Objective peak signal-to-noise ratio performance of the compression technique was analyzed. Results indicate that good reconstructed image quality can be achieved at compression ratios of up to 15:1 for the image types studied. This technique represents an effective approach to the compression of diagnostic medical images and is worthy of further, more thorough, evaluation of diagnostic quality and accuracy in a clinical setting.