Analysis of Image Compression Techniques

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SINCO: A Novel structural regularizer for image compression using implicit neural representations

Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by training an INR model with fewer weights than the number of image pixels to map the coordinates of the image to corresponding pixel values. While traditional training approaches for INRs are based on enforcing pixel-wise image consistency, we propose to further improve image quality by using a new structural regularizer. We present structural regularization for INR compression (SINCO) as a novel INR method for image compression. SINCO imposes structural consistency of the compressed images to the groundtruth by using a segmentation network to penalize the discrepancy of segmentation masks predicted from compressed images. We validate SINCO on brain MRI images by showing that it can achieve better performance than some recent INR methods

Motion Reference Image JPEG2000 : Road surveillance Application with wireless device

WOS:000232176403013International audienceThis paper deals with a new codec based on the JPEG 2000 standard that will use a market hardware codec in order to build a road surveillance device. The developed coder consists in 4 processing steps, namely construction of a reference image, foreground extraction (ROI mask), encoding with JPEG 2000 and transmission through a wireless device. A …rst order recursive …lter is used to build a reference image that corresponds to the background image and the updated reference image is computed according to a mixture of Gaussians model. The system builds a reference image and transmits it towards a decoder through the GSM network. After the initialization phase, the reference image is updated automatically according to a Gaussian mixture model, and when the ROI can be considered as null, a piece of the updated background image is sent. We perform motion detection in order to extract a binary mask. The motion mask gives the region of interest for the system. The current image and the motion mask are coded using the ROI option of JPEG 2000 codec with a very low bit rate and transmitted towards the decoder. The complete scheme is implemented and it reaches the expected performances. We also showed how the local background image is built and updated at each frame. We presented the strategy in order to update smoothly the remote background image. The implementation runs at 5-8 frames per second on a 1.8 GHz AMD processor for 320x240 color images

Improving Embedded Image Coding Using Zero Block - Quad Tree

The traditional multi-bitstream approach to the heterogeneity issue is very constrained and inefficient under multi bit rate applications. The multi bitstream coding techniques allow partial decoding at a various resolution and quality levels. Several scalable coding algorithms have been proposed in the international standards over the past decade, but these former methods can only accommodate relatively limited decoding properties. To achieve efficient coding during image coding the multi resolution compression technique is been used. To exploit the multi resolution effect of image, wavelet transformations are devolved. Wavelet transformation decompose the image coefficients into their fundamental resolution, but the transformed coefficients are observed to be non-integer values resulting in variable bit stream. This transformation result in constraint bit rate application with slower operation. To overcome stated limitation, hierarchical tree based coding were implemented which exploit the relation between the wavelet scale levels and generate the code stream for transmission