AIVC: Artificial Intelligence Based Video Codec

Ladune, Théo; Philippe, Pierrick

doi:10.1109/icip46576.2022.9897240

Cited by 16 publications

(6 citation statements)

References 16 publications

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“…where ŷik|j is the latent coefficient at the position i of level k of description j and c ik|j ∈ Z C is the set of the C decoded coefficients neighboring ŷik|j , representing decoding context. The auto-regressive model p ψ , estimated through a MLP, uses the Laplace distribution as described in [11] to approximate the real conditional probability of the latent space and by using the factorized model defined in (7). The rate for each description ŷj can be expressed as:…”

Section: Autoregressive Modelmentioning

confidence: 99%

“…In recent works on image compression using neural networks, the so-called Implicit Neural Representation (INR), learns to represent an image implicitly through the weights of the INR model, a coordinate map, and possibly a latent space [9,10]. More recently, the Coordinate-based Low Complexity Hierarchical Image Codec (COOL-CHIC) framework [11] has achieved superior performance compared to traditional image compression methods. The first MD scheme using INR (INR-MDSQC) has been proposed in [12] with the following advantages: generalized model training is unnecessary, high performance and flexible redundancy tuning.…”

Section: Introductionmentioning

confidence: 99%

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Implicit Neural Multiple Description for DNA-Based Data Storage

Le,

Pic,

Mateos

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.

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Section: Autoregressive Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implicit Neural Multiple Description for DNA-Based Data Storage

Le,

Pic,

Mateos

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…As an additional experiment we apply HNeR-S for compression, driven by the demand to store daily-growing high-resolution climate and weather datasets (Huang & Hoefler, 2023). To this end, we combine our method with the existing HNeR-based compression scheme (Ladune et al, 2023) to apply image-wise compression, i.e., we train one neural network for the compression of each image. As a non-machine learning baseline, we consider a modular composable SZ3 (Liang et al, 2022) framework.…”

Section: Weather and Climate Datamentioning

confidence: 99%

Relationship of Neural Correlates of Gait Characteristics and Cognitive Dysfunction in Patients With Mild Cognitive Impairment

Kim¹,

Park²,

Choi³

et al. 2023

Preprint

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Background Some patients with mild cognitive impairment (MCI) experience gait disturbances. However, there are few reports on the relationship between gait disturbance and cognitive function in patients with MCI. Therefore, we investigated the neural correlates of gait characteristics related to cognitive dysfunction. Methods Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of 0.5 or higher, with a memory domain score of 0.5 or greater. The patients were classified as having either early or late MCI based on their Mini Mental Status Examination z-scores. Multiple logistic regression analysis was performed to examine the association between the gait characteristics and cognitive impairment. Analyses included variables such as age, sex, years of education, number of comorbidities, body mass index and height. Results Gait velocity, step count, step length, heel-to-heel base support, swing and stance phase duration, and support time were associated with cognitive function. A decrease in gray matter volume in the right pericalcarine area was associated with gait characteristics related to cognitive dysfunction. An increase in the curvature of gray matter in the right entorhinal, right lateral orbitofrontal, right cuneus, and right and left pars opercularis areas was also associated with gait characteristics related to cognitive dysfunction. Conclusion Since gait impairment is an important factor in determining activities of daily living in patients with mild cognitive impairment, the evaluation of gait and cognitive functions in patients with mild cognitive impairment is important.

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“…The "base coder" with no spatial rate allocation strategy is the main anchor of the experiments in this section. To this end, an open-source LVC called AIVC which has shown a competitive performance in the CLIC challenge of the past few years, has been used [16].…”

Section: A Anchormentioning

confidence: 99%

Spatial Rate Allocation for Learning-based Video Coding

Abdoli,

Henry,

Clare

et al. 2023

2023 31st European Signal Processing Conference (EUSIPCO)

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This paper presents a method that enables arbitrary end-to-end Learning-based image/video codecs to apply spatial rate allocation. At the frame-level, the forward pass of the underlying encoder network is followed by a latent refinement step, in which a customized loss function is minimized. This loss function takes as input an arbitrary pixel-wise map that defines the interest of each pixel and computes a weighted distortion with respect to the given interest map. Back-propagation of the customized loss function using the gradient descent gives a refined version of the frame latent in which the quality of regions of interest (ROI) is improved at the cost of quality of regions of disinterest. The proposed method is implemented on top of an existing end-to-end LVC, called AIVC 1 , using saliencebased interest maps. Experiments show that the proposed method can effectively improve the quality of regions of interest frames. Notably, BD-BR performance using Weighted PSNR (WPSNR) shows an improvement of up to 21% by the proposed method.

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AIVC: Artificial Intelligence Based Video Codec

Cited by 16 publications

References 16 publications

Implicit Neural Multiple Description for DNA-Based Data Storage

Implicit Neural Multiple Description for DNA-Based Data Storage

Relationship of Neural Correlates of Gait Characteristics and Cognitive Dysfunction in Patients With Mild Cognitive Impairment

Spatial Rate Allocation for Learning-based Video Coding

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