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Image compression plays more and more important role in image processing. Image sparse coding with learned over-complete dictionaries shows promising results on image compression by representing images with dictionary atoms compactly. Within the sparse coding based compression framework, a sparse dictionary is first learned from training images in a predefined image library, and then an image is compressed...
This work presents images encoding and decoding using the theory of conformal mapping. The conformal mapping theory made changes in the domain of problems without modifying physical characteristics between the domains. Images were utilized and are transported between domains using transformation functions like encrypt keys. Developed method showed to be able to preserve original images characteristics...
To compress large datasets of high-dimensional descriptors, modern quantization schemes learn multiple codebooks and then represent individual descriptors as combinations of codewords. Once the codebooks are learned, these schemes encode descriptors independently. In contrast to that, we present a new coding scheme that arranges dataset descriptors into a set of arborescence graphs, and then encodes...
The paper is focused on use of Pulse Coupled Neural Network (PCNN) in the image steganography based on the research in the field of invariant image recognition. In general, steganography deals with data concealing in the cover mediums which can be freely accessible or transmitted by various communication channels without any restriction. A suitable position of hidden message is crucial for a successful...
Existing methods for layer-based backward compatible high dynamic range (HDR) image and video coding mostly focus on the rate-distortion optimization of base layer while neglecting the encoding of the residue signal in the enhancement layer. Although some recent studies handle residue coding by designing function based fixed global mapping curves for 8-bit conversion and exploiting standard codecs...
In this paper, two novel hardware architectures based on tabled asymmetric numeral systems decoding algorithm are proposed. In the proposed architectures the decoding throughput is highly dependent on the how much the data is compressed at encoding time. The synthesis results presented here show that the throughput of the parallel architecture can reach up 200 MB/s. The benchmarks show that the parallel...
Recent advances in capturing and display technologies, as well as the proliferation of platforms to share images on the Internet, will further increase the bandwidth and storage space required by image coding based applications. To reduce the image coding rate, some techniques taking into account the properties of the human visual system can be used. In this context, this paper proposes an inpainting...
Lossy image compression methods always introduce various unpleasant artifacts into the compressed results, especially at low bit-rates. In recent years, many effective soft decoding methods for JPEG compressed images have been proposed. However, to the best of our knowledge, very few works have been done on soft decoding of JPEG 2000 compressed images. Inspired by the outstanding performance of Convolution...
The main aim of image compression is to represent the image at minimum amount of bytes. This paper presents a new algorithm that reduces number of bytes required to represent images. The proposed system divides the color image into RGB components, CMY components, YCbCr components separately and DCT and DWT are applied to each component and arithmetic coding is applied to the resultant and then their...
This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural...
Linear index coding is generally more robust against channel variations as compared to the fixed-to-variable length coding. This paper proposes a novel multi-pass decoding approach to decode linear index coded images. In contrast to the typical one-pass decoding, the proposed scheme harnesses the information recovered in the first decoding pass with the source statistics and utilize it in the subsequent...
We consider a joint source-channel decoding (JSCD) problem where the source encoder leaves residual redundancy in the source. We first model the redundancy in the source encoder output as the output of a side information channel at the channel decoder, and show that this improves random error exponent. Then, we consider the use of polar codes in this framework when the source redundancy is modeled...
In this article, we resolve open problem 8.2 in [1]. We show that superposition coding is sub-optimal for a three receiver broadcast channel with two message sets (M0, M1) where two of the three receivers need to decode messages M0, M1) while the remaining one just needs to decode the message M0.
Compression of multispectal images is of great importance in an environment where resources such as computational power and memory are scarce. To that end, we propose a new extremely low-complexity encoding approach for compression of multispectral images, that shifts the complexity to the decoding. Our method combines principles from compressed sensing and distributed source coding. Specifically,...
With the rapid growth of image transmission over the public networks, image compression and encryption has received increasing attention to reduce the redundancy and ensure security. In this paper, a scheme of image compression and encryption based on vector quantization (VQ) and cross chaotic map has been proposed that simultaneously compresses and encrypts the image based on confusion-diffusion...
Corner-like features are important in computer vision problems such as object matching, tracking, recognition, and retrieval. Most corner detectors operate in the pixel domain, which means that they require image or video to be fully decoded and reconstructed before detection can start. In this paper we describe a method for generating corner proposals from compressed HEVC bitstreams without full...
Traditional stacked autoencoders have an equal number of encoders and decoders. However, while fine-tuned as a deep neural network the decoder portion is detached and never used. This begs the question: ‘do we need equal number of decoders and encoders’? In this study we explore asymmetric autoencoders — unequal number of encoders and decoders. We specifically address two tasks — 1. Classification...
Network-on-Chip (NoC) is basic part of given system. It is substitution for System-on-Chip to decrease the complexity. Large numbers of different data packets are sent at a time through different links, known as parallelism. But instead of degrading the performance, NoC keeps on growing in performance and scalability. In nanometer CMOS technology, interconnection of links dominates both performance...
An important feature of today's mobile devices is their ability to capture and display high resolution photos in an acceptable amount of time. These images are stored in flash memory on the mobile device using the JPEG codec which is almost a quarter of a century old but remains the industry standard. With increasing pixel counts on both mobile image sensors and screens, software solutions will struggle...
We present Marlin, a variable-to-fixed (VF) codec optimized for decoding speed. Marlin builds upon a novel way of constructing VF dictionaries that maximizes efficiency for a given dictionary size. On a lossless image coding experiment, Marlin achieves a compression ratio of 1.94 at 2494MiB/s. Marlin is as fast as state-of-the-art high-throughput codecs (e.g., Snappy, 1.24 at 2643MiB/s), and its compression...
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