This paper proposes an improved image high fidelity compression algorithm based on the generative adversarial networks (GANs) to deal with the problem that the UAV image has a large amount of data which is not conducive to post-processing. By adding an encoder in front of the generator, the disaster area image transmitted by UAV is compressed to meet the requirements of the generator. After the compressed image is trained together with the real image through the discriminator, the quality of the compressed image is constantly improved. This image compression algorithm can fully synthesize the codes of non-major areas such as trees and rivers in the image, and try to retain the codes of important areas such as houses and roads. The experimental results show that the proposed compression method in this paper has a higher compression ratio than the traditional compression method for the disaster area image, and can obtain images with strong sense of hierarchy.

/pdf/uav-image-high-fidelity-compression-algorithm-based-on-4nivg1dilf.pdf

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

With the increasing amount of HD or even UHD video, the video streaming transmission over the mobile network is confronted with various quality of experience issues. To facilitate the delivery of compressed video over the network, it is extremely helpful to further compress the coded video bit stream without any information loss, so-called lossless recompression. However, the existing lossless video/image compression algorithms aim to remove the pixel-domain (e.g., lossless coding mode of HEVC) or the coefficient-domain (e.g., entropy coding) redundancy rather than the bit-stream-domain redundancy. To this end, we propose a novel lossless recompression approach to eliminate statistical redundancy in the coded video bit stream. The core idea is to increase the probability of this repetitive pattern of continuous “0” or “1” symbol strings in the binary symbol sequence by rearranging the sequence, including value mapping and position aggregation. In particular, Fibonacci-based mapping rule is used to convert the original sequence (those binary symbols in Fibonacci positions) into a new one with more continuous “0” or “1.” Besides, we aggregate as many identical symbols as possible together to further increase the probability of the occurrence of repetitive patterns. Finally, the lossless compression algorithm and the asymptotic lossless compression scheme are designed to achieve a compact representation of the rearranged symbol sequence. We also regulate formal syntactic structures for the proposed mapping rule, aggregation algorithm, and recompression scheme so as to deterministically revert from the recoded version to the original bit stream. The experimental results on compressed H.264/AVC and H.265/HEVC video data show that our approach can considerably reduce the bit rate of streaming video.

A Lossless Recompression Approach for Video Streaming Transmission

The three main pillars for lossless image compression are prediction, prediction residual correction and entropy coding. In this work, we propose a lossless image compression algorithm which utilizes local information to dynamically switch between different prediction techniques. Moreover, we improve on traditional prediction techniques and modify them into diagonal GAP and dynamic window weighted linear prediction. Finally, since the coding efficiency is directly proportional to the context being chosen if context arithmetic coding is applied, we propose a robust context model which utilizes local information to get better coding efficiency.

Adaptive Prediction, Context Modeling, and Entropy Coding Methods for CALIC Lossless Image Compression

Arithmetic Coding (AC) is a form of entropy encoding used in lossless data compression. It is a well-known, state of the art technique, in which the frequently seen symbols are encoded with fewer bits than rarely seen symbols. It has been widely used since last four decades. Many researchers worked on it to improve its performance and they successfully experimented with it. This technique has also been in use in combination with other techniques to gain surprising results. In this survey paper, an effort is made to recap a number of accomplishments from 1976 to 2017 regarding Arithmetic Coding. This study provides an insight for new researchers to know how this technique evolved with time and how major achievements were made using this technique. This paper gives a comparison of AC with another well-known technique named Huffman Coding. Comparison with its contemporary counterparts shows that it is better in performance almost in every situation.

/pdf/evolution-and-advancement-of-arithmetic-coding-over-four-qxe5d5l7fw.pdf

Evolution and Advancement of Arithmetic Coding over Four Decades

Electroencephalogram signals reveals the activity of brain and is an important medial signal. In this paper, an efficient compression algorithm for the electroencephalogram signal is proposed. In addition to the improved adaptive arithmetic coding scheme we developed recently, the proposed algorithm also adopts the novel techniques of singular point segmentation, principal coefficient selection, the zero-and-integer approaching scheme, adaptive zero-based run-length coding, and the coding scheme with long-length coding capability. The proposed compression algorithm tested in the electroencephalogram signal database and the results show that the reconstruction error of the proposed algorithm is less than 50% of that of state-of-the-art methods when the compression ratio is 13.66.

Efficient EEG Signal Compression Algorithm with Long Length Improved Adaptive Arithmetic Coding and Advanced Division and Encoding Techniques

Context-based adaptive arithmetic coding (CAAC) has high coding efficiency and is adopted by the majority of advanced compression algorithms. In this paper, five new techniques are proposed to further improve the performance of CAAC. They make the frequency table (the table used to estimate the probability distribution of data according to the past input) of CAAC converge to the true probability distribution rapidly and hence improve the coding efficiency. Instead of varying only one entry of the frequency table, the proposed range-adjusting scheme adjusts the entries near to the current input value together. With the proposed mutual-learning scheme, the frequency tables of the contexts highly correlated to the current context are also adjusted. The proposed increasingly adjusting step scheme applies a greater adjusting step for recent data. The proposed adaptive initialization scheme uses a proper model to initialize the frequency table. Moreover, a local frequency table is generated according to local information. We perform several simulations on edge-directed prediction-based lossless image compression, coefficient encoding in JPEG, bit plane coding in JPEG 2000, and motion vector residue coding in video compression. All simulations confirm that the proposed techniques can reduce the bit rate and are beneficial for data compression.

Improved Efficiency on Adaptive Arithmetic Coding for Data Compression Using Range-Adjusting Scheme, Increasingly Adjusting Step, and Mutual-Learning Scheme

Block matching based motion estimation (BMME) is widely used in video compression and stereoscopic image processing. Given that the full search algorithm is not efficient enough, many fast motion estimation algorithms are based on BMME. Adaptive rood path search (ARPS), which is an important process of the BMME method, exploits the inter-block correlation and zero-motion prejudgment to reduce the search cost. Although ARPS is very efficient, there is a gap between ARPS and full search in accuracy. In this manuscript, we propose a novel adaptive rood path search (N-ARPS) algorithm with small-motion prejudgment (SMP). SMP is to select appropriate search strategy for each block according to its motion properties. In addition, region of support (ROS) expansion is applied to have a better prediction for the initial search. Experimental results show that the proposed fast search algorithm has a near-optimal search accuracy and extremely lower computational complexity when compared with standard-of-the-art algorithms.

Novel Adaptive Rood Path Searches with Small Motion Prejudgments for Fast Block Motion Estimation

Motion estimation is an essential procedure in video coding and object tracing, but it always has a high computational load. Some low bit-depth motion estimation methods, such as one/two-bit transform or gray coding based methods, have lower complexity. However, one-bit transform based methods are sensitive to noise, whereas two-bit transform and gray coding methods use many bit planes or operations to perform motion estimation. In this paper, we select features with high representative by two means. First, we present an improved one-bit transform with pre-processing techniques. Second, an adaptive matching criterion was introduced to pick out features with higher representative. Moreover, a fast search algorithm is also proposed to reduce the searching cost. The experimental results show that the proposed algorithm outperforms other methods both in matching accuracy and in computation efficiency.

Advanced one-bit transform algorithm with edge enhancement and representative feature extraction for low-complexity and accurate motion estimation

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Improved Efficiency on Adaptive Arithmetic Coding for Data Compression Using Range-Adjusting Scheme, Increasingly Adjusting Step, and Mutual-Learning Scheme

Novel Adaptive Rood Path Searches with Small Motion Prejudgments for Fast Block Motion Estimation

Advanced one-bit transform algorithm with edge enhancement and representative feature extraction for low-complexity and accurate motion estimation