深層学習に基づく画像圧縮と品質評価

早大学位記番号:新8427早稲田大

Mobile app with steganography functionalities

[Abstract]: Steganography is the practice of hiding information within other data, such as images, audios, videos, etc. In this research, we consider applying this useful technique to create a mobile application that lets users conceal their own secret data inside other media formats, send that encoded data to other users, and even perform analysis to images that may have been under a steganography attack. For image steganography, lossless compression formats employ Least Significant Bit (LSB) encoding within Red Green Blue (RGB) pixel values. Reciprocally, lossy compression formats, such as JPEG, utilize data concealment in the frequency domain by altering the quantized matrices of the files. Video steganography follows two similar methods. In lossless video formats that permit compression, the LSB approach is applied to the RGB pixel values of individual frames. Meanwhile, in lossy High Efficient Video Coding (HEVC) formats, a displaced bit modification technique is used with the YUV components.[Resumo]: A esteganografía é a práctica de ocultar determinada información dentro doutros datos, como imaxes, audio, vídeos, etc. Neste proxecto pretendemos aplicar esta técnica como visión para crear unha aplicación móbil que permita aos usuarios ocultar os seus propios datos secretos dentro doutros formatos multimedia, enviar eses datos cifrados a outros usuarios e mesmo realizar análises de imaxes que puidesen ter sido comprometidas por un ataque esteganográfico. Para a esteganografía de imaxes, os formatos con compresión sen perdas empregan a codificación Least Significant Bit (LSB) dentro dos valores Red Green Blue (RGB) dos seus píxeles. Por outra banda, os formatos de compresión con perdas, como JPEG, usan a ocultación de datos no dominio de frecuencia modificando as matrices cuantificadas dos ficheiros. A esteganografía de vídeo segue dous métodos similares. En formatos de vídeo sen perdas, o método LSB aplícase aos valores RGB de píxeles individuais de cadros. En cambio, nos formatos High Efficient Video Coding (HEVC) con compresión con perdas, úsase unha técnica de cambio de bits nos compoñentes YUV.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202

Algorithms for compression of high dynamic range images and video

The recent advances in sensor and display technologies have brought upon the High Dynamic Range (HDR) imaging capability. The modern multiple exposure HDR sensors can achieve the dynamic range of 100-120 dB and LED and OLED display devices have contrast ratios of 10^5:1 to 10^6:1. Despite the above advances in technology the image/video compression algorithms and associated hardware are yet based on Standard Dynamic Range (SDR) technology, i.e. they operate within an effective dynamic range of up to 70 dB for 8 bit gamma corrected images. Further the existing infrastructure for content distribution is also designed for SDR, which creates interoperability problems with true HDR capture and display equipment. The current solutions for the above problem include tone mapping the HDR content to fit SDR. However this approach leads to image quality associated problems, when strong dynamic range compression is applied. Even though some HDR-only solutions have been proposed in literature, they are not interoperable with current SDR infrastructure and are thus typically used in closed systems. Given the above observations a research gap was identified in the need for efficient algorithms for the compression of still images and video, which are capable of storing full dynamic range and colour gamut of HDR images and at the same time backward compatible with existing SDR infrastructure. To improve the usability of SDR content it is vital that any such algorithms should accommodate different tone mapping operators, including those that are spatially non-uniform. In the course of the research presented in this thesis a novel two layer CODEC architecture is introduced for both HDR image and video coding. Further a universal and computationally efficient approximation of the tone mapping operator is developed and presented. It is shown that the use of perceptually uniform colourspaces for internal representation of pixel data enables improved compression efficiency of the algorithms. Further proposed novel approaches to the compression of metadata for the tone mapping operator is shown to improve compression performance for low bitrate video content. Multiple compression algorithms are designed, implemented and compared and quality-complexity trade-offs are identified. Finally practical aspects of implementing the developed algorithms are explored by automating the design space exploration flow and integrating the high level systems design framework with domain specific tools for synthesis and simulation of multiprocessor systems. The directions for further work are also presented

Image and Video Coding Techniques for Ultra-low Latency

The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.publishedVersionPeer reviewe

Optimum Implementation of Compound Compression of a Computer Screen for Real-Time Transmission in Low Network Bandwidth Environments

Remote working is becoming increasingly more prevalent in recent times. A large part of remote working involves sharing computer screens between servers and clients. The image content that is presented when sharing computer screens consists of both natural camera captured image data as well as computer generated graphics and text. The attributes of natural camera captured image data differ greatly to the attributes of computer generated image data. An image containing a mixture of both natural camera captured image and computer generated image data is known as a compound image. The research presented in this thesis focuses on the challenge of constructing a compound compression strategy to apply the ‘best fit’ compression algorithm for the mixed content found in a compound image. The research also involves analysis and classification of the types of data a given compound image may contain. While researching optimal types of compression, consideration is given to the computational overhead of a given algorithm because the research is being developed for real time systems such as cloud computing services, where latency has a detrimental impact on end user experience. The previous and current state of the art videos codec’s have been researched along many of the most current publishing’s from academia, to design and implement a novel approach to a low complexity compound compression algorithm that will be suitable for real time transmission. The compound compression algorithm will utilise a mixture of lossless and lossy compression algorithms with parameters that can be used to control the performance of the algorithm. An objective image quality assessment is needed to determine whether the proposed algorithm can produce an acceptable quality image after processing. Both traditional metrics such as Peak Signal to Noise Ratio will be used along with a new more modern approach specifically designed for compound images which is known as Structural Similarity Index will be used to define the quality of the decompressed Image. In finishing, the compression strategy will be tested on a set of generated compound images. Using open source software, the same images will be compressed with the previous and current state of the art video codec’s to compare the three main metrics, compression ratio, computational complexity and objective image quality

JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS

The massive volume of data generated daily by the gathering of medical images with different modalities might be difficult to store in medical facilities and share through communication networks. To alleviate this issue, efficient compression methods must be implemented to reduce the amount of storage and transmission resources required in such applications. However, since the preservation of all image details is highly important in the medical context, the use of lossless image compression algorithms is of utmost importance. This thesis presents the research results on a lossless compression scheme designed to encode both computerized tomography (CT) and positron emission tomography (PET). Different techniques, such as image-to-image translation, intra prediction, and inter prediction are used. Redundancies between both image modalities are also investigated. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Two approaches were implemented and evaluated to determine a PET residue that will be compressed along with the original CT. In the first method, the residue resulting from the differences between the original PET and its estimation is encoded, whereas in the second method, the residue is obtained using encoders inter-prediction coding tools. Thus, in alternative to compressing two independent picture modalities, i.e., both images of the original PET-CT pair solely the CT is independently encoded alongside with the PET residue, in the proposed method. Along with the proposed pipeline, a post-processing optimization algorithm that modifies the estimated PET image by altering the contrast and rescaling the image is implemented to maximize the compression efficiency. Four different versions (subsets) of a publicly available PET-CT pair dataset were tested. The first proposed subset was used to demonstrate that the concept developed in this work is capable of surpassing the traditional compression schemes. The obtained results showed gains of up to 8.9% using the HEVC. On the other side, JPEG2k proved not to be the most suitable as it failed to obtain good results, having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains, when compared to conventional compression methods, up 6.33% compression gain using HEVC, and 7.78% using VVC

3D coding tools final report

Livrable D4.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.3 du projet. Son titre : 3D coding tools final repor

DCT-based Image/Video Compression: New Design Perspectives

To push the envelope of DCT-based lossy image/video compression, this thesis is motivated to revisit design of some fundamental blocks in image/video coding, ranging from source modelling, quantization table, quantizers, to entropy coding. Firstly, to better handle the heavy tail phenomenon commonly seen in DCT coefficients, a new model dubbed transparent composite model (TCM) is developed and justified. Given a sequence of DCT coefficients, the TCM first separates the tail from the main body of the sequence, and then uses a uniform distribution to model DCT coefficients in the heavy tail, while using a parametric distribution to model DCT coefficients in the main body. The separation boundary and other distribution parameters are estimated online via maximum likelihood (ML) estimation. Efficient online algorithms are proposed for parameter estimation and their convergence is also proved. When the parametric distribution is truncated Laplacian, the resulting TCM dubbed Laplacian TCM (LPTCM) not only achieves superior modeling accuracy with low estimation complexity, but also has a good capability of nonlinear data reduction by identifying and separating a DCT coefficient in the heavy tail (referred to as an outlier) from a DCT coefficient in the main body (referred to as an inlier). This in turn opens up opportunities for it to be used in DCT-based image compression. Secondly, quantization table design is revisited for image/video coding where soft decision quantization (SDQ) is considered. Unlike conventional approaches where quantization table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a quantization table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a quantization table can be optimized in a way that the resulting distortion complies with certain behavior, yielding the so-called optimal distortion profile scheme (OptD). Guided by this new theoretical result, we present an efficient statistical-model-based algorithm using the Laplacian model to design quantization tables for DCT-based image compression. When applied to standard JPEG encoding, it provides more than 1.5 dB performance gain (in PSNR), with almost no extra burden on complexity. Compared with the state-of-the-art JPEG quantization table optimizer, the proposed algorithm offers an average 0.5 dB gain with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain or more with 85% of the complexity reduced when SDQ is on. Thirdly, based on the LPTCM and OptD, we further propose an efficient non-predictive DCT-based image compression system, where the quantizers and entropy coding are completely re-designed, and the relative SDQ algorithm is also developed. The proposed system achieves overall coding results that are among the best and similar to those of H.264 or HEVC intra (predictive) coding, in terms of rate vs visual quality. On the other hand, in terms of rate vs objective quality, it significantly outperforms baseline JPEG by more than 4.3 dB on average, with a moderate increase on complexity, and ECEB, the state-of-the-art non-predictive image coding, by 0.75 dB when SDQ is off, with the same level of computational complexity, and by 1 dB when SDQ is on, at the cost of extra complexity. In comparison with H.264 intra coding, our system provides an overall 0.4 dB gain or so, with dramatically reduced computational complexity. It offers comparable or even better coding performance than HEVC intra coding in the high-rate region or for complicated images, but with only less than 5% of the encoding complexity of the latter. In addition, our proposed DCT-based image compression system also offers a multiresolution capability, which, together with its comparatively high coding efficiency and low complexity, makes it a good alternative for real-time image processing applications