Efficient Image Compression Method Research Articles

Efficient image compression method using image super-resolution residual learning network

With the rapid growth of Internet video image information, there is a large amount of redundancy in image data. Use less data stream information to transfer the image or the amount of information contained in the image. Its purpose is to reduce the redundancy of images, so as to store them at low bit rate and reduce the data storage space. In the general image compression method, the hybrid coding framework is adopted. Each algorithm adopts a fixed algorithm mode through a specific design algorithm, without global optimization. Image compression is mainly divided into prediction, transformation, quantization, digital entropy coding and other steps. At present, there are many researches on super-resolution network based on deep learning technology. The main function is to reconstruct high-resolution image replace image magnification low-resolution images such as linear interpolation, which has a great performance improvement image resolution, noise reduction, deblurring and so on, but there is no effective way to use super-resolution network applications to improve quality of compression reconstructed image quality. This paper involves a new method that using image super-resolution residual learning network to improve quality of compression image, our method, the reduced image is encoded into a content stream and a transmission corresponding parameter is encoded into a model stream. Firstly, the original image is scaled down 1/2 size of source image, then encode the small image into content stream with the existing codec. Secondly, the residual learning super-resolution (SR) network is used for image filtering to scale up reconstructed image with decode image resizing method and boost the quality of edge feature extraction of image. Our results show that there is significant performance improvement of h265 in low resolution reconstructed image (bits-per-pixel less than 0.1).

Improving Image Compression With Adjacent Attention and Refinement Block

Recently, learned image compression algorithms have shown incredible performance compared to classic hand-crafted image codecs. Despite its considerable achievements, the fundamental disadvantage is not optimized for retaining local redundancies, particularly non-repetitive patterns, which have a detrimental influence on the reconstruction quality. This paper introduces the autoencoder-style network-based efficient image compression method, which contains three novel blocks, i.e., adjacent attention block, Gaussian merge block, and decoded image refinement block, to improve the overall image compression performance. The adjacent attention block allocates the additional bits required to capture spatial correlations (both vertical and horizontal) and effectively remove worthless information. The Gaussian merge block assists the rate-distortion optimization performance, while the decoded image refinement block improves the defects in low-resolution reconstructed images. A comprehensive ablation study analyzes and evaluates the qualitative and quantitative capabilities of the proposed model. Experimental results on two publicly available datasets reveal that our method outperforms the state-of-the-art methods on the KODAK dataset (by around 4dB and 5dB) and CLIC dataset (by about 4dB and 3dB) in terms of PSNR and MS-SSIM.

Efficient Light Field Image Compression with Enhanced Random Access

In light field image compression, facilitating random access to individual views plays a significant role in decoding views quickly, reducing memory footprint, and decreasing the bandwidth requirement for transmission. Highly efficient light field image compression methods mainly use inter view prediction. Therefore, they typically do not provide random access to individual views. On the other hand, methods that provide full random access usually reduce compression efficiency. To address this trade-off, a light field image encoding method that favors random access is proposed in this paper. Light field image views are grouped into independent (3× 3) views, which are called Macro View Images (MVIs) . To encode MVIs, the central view is used as a reference to compress its adjacent neighboring views using a hierarchical reference structure. To encode the central view of each MVI, the most central view along with the center of a maximum of three MVIs, are used as reference images for the disparity estimation. In addition, the proposed method allows the use of parallel processing to reduce the maximum encoding/decoding time-complexity in multi-core processors. Tile partitioning can also be used to randomly access different regions of the light field images. The simulation results show that the proposed method outperforms other state-of-the-art methods in terms of compression efficiency while providing random access to both views and regions of interest.

Rate–distortion optimal evolutionary algorithm for JPEG quantization with multiple rates

The JPEG standard is used extensively in image-related applications. The optimization on a JPEG-standard compressor is of high importance for image storage and transmission. In this paper, we propose a versatile and efficient quantization optimization method for image compression. It is based on multi-objective optimization and is JPEG standard-compatible. Unlike the existing optimization methods, our method fully considers the rate–distortion optimal principle and provides several optimal solutions to address the multiple-rate requirement in applications. In the multi-objective evolutionary optimization framework, the fitness of each quantization table for JPEG compression is evaluated efficiently by the searching in a look-up table, which is constructed based on the statistics of each DCT band in a pre-defined manner. Then, the population update strategy based on the rate–distortion optimal principle is recommended to guide the evolution toward the best rate–distortion performance. Furthermore, to maintain the population’s diversity and uniformity, convex-hull based environmental selection is recommended to identify the solutions at the first Pareto front, and the identified solutions are enriched further with scaling operation to fill the blank rate range. The experimental results for several classic datasets demonstrate the superiority of our method in terms of solution distribution, coding efficiency, and computational complexity.

Iterative Enhanced Multivariance Products Representation for Effective Compression of Hyperspectral Images

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods.

Optimized color space for image compression based on DCT and Bat algorithm

This paper develops an efficient color image compression method based on the DCT and a new color base. Digital color images are commonly represented in RGB space. Generally, it is noted that a strong correlation exists between the three planes R, G, and B of a color image. The reduction of this correlation certainly offers an advantage in the compression of RGB images. In this context, there is an infinite number of possible spaces to represent the RGB image. The main contributions of this paper are summarized in two main points. First, we design an optimized color space B1B2B3 using the Bat algorithm (BA) to pass from the RGB space to space more appropriate for each image. This space is produced by maximizing the energy of the image in the plane B1 more than in B2 and B3. Second, we produce optimized thresholds appropriate to each plane of the converted image. The Bat algorithm optimizes the cost function to compute thresholds to partially reduce the number of the less significant DCT coefficients that correspond to the lower quantity of energy. The reported results against those of recent methods prove that the proposed method presents high performances in terms of peak signal to noise ratio (PSNR) on the commonly used test color images as well as the test medical images in literature.

An Efficient Compression Method of Hyperspectral Images Based on Compressed Sensing and Joint Optimization

Compressed sensing provides the possibility of efficient compression of massive hyperspectral data. However, the existing methods often use the organization pattern of image blocks in sparse representation, and cannot make full use of inter spectral correlation. The separate retrieval of dictionary and measurement matrix also restricts the processing efficiency. To solve these problems, a novel and efficient hyperspectral image compression method based on compressed sensing and joint optimization is proposed in this paper. In sparse representation, the data organization pattern on spectral dimension is adopted to better express the correlation between spectra and improve the efficiency of operation. On the dictionary and measurement matrix, a joint optimization algorithm is proposed, which synchronously inhibits the sparse representation error and the reconstruction error. Experimental results show that compared with similar methods, the reconstruction error of this method is increased by 3 dB and the number of iterations is reduced by seven times, and the compression rate can reach 1/18.

An efficient lossy cartoon image compression method

This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm.

An Efficient Image Compression Method by Using Optimized Discrete Wavelet Transform and Huffman Encoder

An Efficient Image Compression Method by Using Optimized Discrete Wavelet Transform and Huffman Encoder

Multidimensional Compression of ITS Data Using Wavelet-Based Compression Techniques

This paper explores the use of wavelet transform-based methods for ITS data compression. A methodology for structuring data and applying wavelet transform-based algorithms is proposed. The methodology provides the option of controlling the compression ratio at the cost of an acceptable distortion, visualizing data at different detail levels. With proper database management, this methodology will also allow faster data access without fully decompressing them. Given a high correlation of traffic data and knowing that the image data are compressed very well due to the inherent correlation of image pixels, the idea here is to restructure the traffic data, such that efficient image compression methods underlying modern image compression standards can be used. Three data structures are discussed: 1-D, 2-D, and 3-D. For a 1-D arrangement, different wavelets and decomposition levels were tested and analyzed for distortion levels in the data after decompression. The 2-D and 3-D data arrangements were compressed using embedded zero-tree wavelet and set partitioning in hierarchical trees algorithms, which are well-proved algorithms for compressing image data. A case study was performed using the traffic flow data from freeways in Las Vegas, Nevada. As could be expected, the compression ratio under the 3-D scheme has shown the best results. The 2-D and 3-D approaches yielded a 91% and 95.2% reduction ratios, respectively.

An Improved Run Length Encoding Scheme for Image Compression

In this paper, we propose an Improved Run-Length Encoding (IRLE) scheme for image compression. Conventional RunLength Encoding is a widely used scheme as it is simple and effective but not suitable to represent non-block images. The IRLE which we have developed, combines runs and run values and it reduces the space required to store the image significantly. The compression ratio depends on the size and type of the input message. From the results obtained in this analysis, we conclude that the proposed approach is an efficient method for image compression and it provides high compression ratio and better compression percentage.

Efficient multi-bands image compression method for remote cameras

In this Letter, we propose an efficient compression algorithm for multi-spectral images having a few bands. First, we propose a low-complexity removing spectral redundancy approach to improve compression performance. Then, a bit plane encoding approach is applied to each band to complete the compression. Finally, the experiments are performed on multi-spectral images. The experiment results show that the proposed compression algorithm has good compressive property. Compared with traditional approaches, the proposed method can decrease the average peak signal noise ratio by 0.36 dB at 0.5 bpp. The processing speed reaches 23.81 MPixels/s at the working frequency of 88 MHz, which is higher than the traditional methods. The proposed method satisfies the project application.

Colour image compression algorithm based on the DCT transform using difference lookup table

A simple and efficient method for lossy colour image compression is proposed. Here, the discrete cosine transform (DCT) is applied to the YCbCr image obtained from the original RGB image. The bisection method is used to define the required threshold for a prefixed user peak signal to noise ratio as a controlled quality criterion. The thresholded and quantised DCT coefficients are encoded with a new technique. The proposed technique uses the difference of the indexes of the retained coefficients in coordination with DCT block adaptive scanning to encode efficiently the coefficients. The difference of the indexes is stored in a lookup table called (dLUT). When compared with recent methods, the obtained results show that the proposed algorithm achieves high performance.

Region of interest extraction for lossless compression of bone X-ray images.

For few decades digital X-ray imaging has been one of the most important tools for medical diagnosis. With the advent of distance medicine and the use of big data in this respect, the need for efficient storage and online transmission of these images is becoming an essential feature. Limited storage space and limited transmission bandwidth are the main challenges. Efficient image compression methods are lossy while the information of medical images should be preserved with no change. Hence, lossless compression methods are necessary for this purpose. In this paper, a novel method has been proposed to eliminate the non-ROI data from bone X-ray images. Background pixels do not contain any valuable medical information. The proposed method is based on the histogram dispersion method. ROI is separated from the background and it is compressed with a lossless compression method to preserve medical information of the image. Compression ratios of the implemented results show that the proposed algorithm is capable of effective reduction of the statistical and spatial redundancies.

Projection-based medical image compression for telemedicine applications.

Recent years have seen great development in the field of medical imaging and telemedicine. Despite the developments in storage and communication technologies, compression of medical data remains challenging. This paper proposes an efficient medical image compression method for telemedicine. The proposed method takes advantage of Radon transform whose basis functions are effective in representing the directional information. The periodic re-ordering of the elements of Radon projections requires minimal interpolation and preserves all of the original image pixel intensities. The dimension-reducing property allows the conversion of 2D processing task to a set of simple 1D task independently on each of the projections. The resultant Radon coefficients are then encoded using set partitioning in hierarchical trees (SPIHT) encoder. Experimental results obtained on a set of medical images demonstrate that the proposed method provides competing performance compared with conventional and state-of-the art compression methods in terms of compression ratio, peak signal-to-noise ratio (PSNR), and computational time.

An Optimal HSI Image Compression using DWT and CP

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. An efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and CANDECOM/PARAFAC (DWT-CP), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply CP on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and CP to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance when comparing with the existing method PCA with JPEG 2000 and 3D SPECK. DOI: http://dx.doi.org/10.11591/ijece.v4i3.6326

Efficient Data Reduction Techniques for Remote Applications of a Wireless Visual Sensor Network

Abstract A Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. After acquiring an image of the area of interest, the VSN performs local processing on it and transmits the result using an embedded wireless transceiver. Wireless data transmission consumes a great deal of energy, where energy consumption is mainly dependent on the amount of information being transmitted. The image captured by the VSN contains a huge amount of data. For certain applications, segmentation can be performed on the captured images. The amount of information in the segmented images can be reduced by applying efficient bi-level image compression methods. In this way, the communication energy consumption of each of the VSNs can be reduced. However, the data reduction capability of bi-level image compression standards is fixed and is limited by the used compression algorithm. For applications attributing few changes in adjacent frames, change coding can be applied for further data reduction. Detecting and compressing only the Regions of Interest (ROIs) in the change frame is another possibility for further data reduction. In a communication system, where both the sender and the receiver know the employed compression standard, there is a possibility for further data reduction by not including the header information in the compressed bit stream of the sender. This paper summarizes different information reduction techniques such as image coding, change coding and ROI coding. The main contribution is the investigation of the combined effect of all these coding methods and their application to a few representative real life applications. This paper is intended to be a resource for researchers interested in techniques for information reduction in energy constrained embedded applications.

Compression of Hyperspectral Images Using Discerete Wavelet Transform and Tucker Decomposition

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. In this paper, an efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and Tucker Decomposition (DWT-TD), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and TD to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance of the proposed method. Moreover, we show the impact of compression HSIs on the supervised classification and linear unmixing.

Enhanced Hybrid Compound Image Compression Algorithm Combining Block and Layer-based Segmentation

This paper presents an efficient compound image compression method based on both block and layer– based segmentation techniques, which introduces a new hybrid scheme for segmenting compound images. In this hybrid model the image is first segmented into five different blocks using block based classification again the overlapping block is segmented using layer based segmentation for improving the compression ratio, visual quality.

A fast near-optimum block truncation coding method using a truncated K-means algorithm and inter-block correlation

AbstractBlock truncation coding (BTC) is an efficient image compression method which finds application in diverse fields. The basic problem can be viewed as a two-level quantization process. However, efficient ways for optimal threshold determination have not been discovered so far. We propose a fast BTC algorithm based on a truncated K-means algorithm, utilizing the image inter-block correlation and the fact that K-means algorithm converges very fast in this case. This produces near-optimum solution with significantly improved speed over other methods. Simulation results confirm such advantages.