Sorry, you need to enable JavaScript to visit this website.

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection.


The VVC codec is applied to the task of multispectral image (MSI) compression using adap- tive and scalable coding structures. In a “plain” VVC approach, concepts from picture-to- picture temporal prediction are employed for decorrelation along the MSI’s spectral dimen- sion. The popular principle component analysis (PCA) for spectral decorrelation is further evaluated in combination with VVC intra-coding for spatial decorrelation. This approach is referred to as PCA-VVC.


In this paper, we consider using a multiscale approach to reduce complexity while maintaining coding efficiency. Experimental results demonstrate a 5.4× reduction in MAC operations while achieving an average bit rate savings of 6.4% and 6.3% for all intra and random access coding, respectively, when compared to the evolving AV2 standard. Ablation studies are also provided and show that the approach achieves all but 0.2% of the coding efficiency of full resolution processing.


Deep variational autoencoders for image and video compression have gained significant attraction
in the recent years, due to their potential to offer competitive or better compression
rates compared to the decades long traditional codecs such as AVC, HEVC or VVC. However,
because of complexity and energy consumption, these approaches are still far away
from practical usage in industry. More recently, implicit neural representation (INR) based
codecs have emerged, and have lower complexity and energy usage to classical approaches at