The availability of massive amounts of data in histopathological whole-slide images (WSIs) has enabled the application of deep learning models and especially convolutional neural networks (CNNs), which have shown a high potential for improvement in cancer diagnosis. However, storage and transmission of large amounts of data such as gigapixel histopathological WSIs are challenging. Exploiting lossy compression algorithms for medical images is controversial but, as long as the clinical diagnosis is not affected, is acceptable. We study the impact of JPEG 2000 compression on our proposed CNN-based algorithm, which has produced performance comparable to that of pathologists and which was ranked second place in the CAMELYON17 challenge. Detecting tumor metastases in hematoxylin and eosin-stained tissue sections of breast lymph nodes is evaluated and compared with the pathologists' diagnoses in three different experimental setups. Our experiments show that the CNN model is robust against compression ratios up to 24:1 when it is trained on uncompressed high-quality images. We demonstrate that a model trained on lower quality images-i.e., lossy compressed images-depicts a classification performance that is significantly improved for the corresponding compression ratio. Moreover, it is also observed that the model performs equally well on all higher-quality images. These properties will help to design cloud-based computer-aided diagnosis (CAD) systems, e.g., telemedicine that employ deep CNN models that are more robust to image quality variations due to compression required to address data storage and transmission constraints. However, the results presented are specific to the CAD system and application described, and further work is needed to examine whether they generalize to other systems and applications.

JPEG Pleno is a standardization framework addressing the compression and signaling of plenoptic modalities. While the standardization of solutions to handle light field content is currently reaching its final stage, the Joint Photographic Experts Group (JPEG) committee is now preparing for the standardization of solutions targeting point cloud and holographic modalities. This paper addresses the challenges related to the standardization of compression technologies for holographic content and associated test methodologies.

Steganography is the art of hiding information in a cover medium such that the existence of information is concealed. An image is a suitable cover medium for steganography because of its great amount of redundant spaces. One simple method of image steganography is the replacement of the least significant bit (LSB) of a cover image with a message bit. This represents a high embedding capacity but it is detectable by statistical analysis methods such as Regular-Singular (RS) and Chi-square analyses. Therefore, a new LSB algorithm is proposed here which can effectively resist statistical analysis. In this novel algorithm, every two sample's LSB bits are combined using addition modulo 2 which is compared to the secret message. If these two values are not equal, their difference is added to the second sample. Otherwise, no change is made. This paper proposes a hardware realization of this new algorithm. Furthermore, two scalable pixel interleaver and novel message bit randomizer with two different stego-keys are designed. Pixel interleaver can improve resistance against visual analysis by random selection of pixels.

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Background subtraction is an important part of various computer vision applications that can detect the foreground objects by comparing the current pixels with a background model. The general approaches gradually update the background model according to the current status, but might fail in sudden illumination changes. An illumination-robust background modelling method is proposed to address this problem. The method provides quick illumination compensation using two background models with different adaption rates. Experimental results show that the proposed algorithm outperforms several state-of-art approaches and provides low computational cost.

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