no code implementations • 8 Oct 2022 • Honglei Zhang, Francesco Cricri, Hamed Rezazadegan Tavakoli, Emre Aksu, Miska M. Hannuksela
Nevertheless, the proposed LIC systems are still inferior to the state-of-the-art traditional techniques, for example, the Versatile Video Coding (VVC/H. 266) standard, due to either their compression performance or decoding complexity.
no code implementations • 16 Dec 2021 • Nannan Zou, Honglei Zhang, Francesco Cricri, Ramin G. Youvalari, Hamed R. Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu
In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention.
no code implementations • 24 Aug 2021 • Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli, Nannan Zou, Emre Aksu, Miska M. Hannuksela
Recently, multi-scale autoregressive models have been proposed to address this limitation.
no code implementations • 31 Jul 2020 • Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. -Tavakoli, Jani Lainema, Miska Hannuksela, Emre Aksu, Esa Rahtu
In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance.
no code implementations • 20 Apr 2020 • Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. -Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu
One of the core components of conventional (i. e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations.
no code implementations • 3 Jul 2019 • Peter Fasogbon, Emre Aksu
This is not necessarily true for special imaging device such as fisheye lenses.
no code implementations • 24 May 2019 • Caglar Aytekin, Francesco Cricri, Antti Hallapuro, Jani Lainema, Emre Aksu, Miska Hannuksela
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss.
no code implementations • 10 May 2019 • Yat Hong Lam, Alireza Zare, Caglar Aytekin, Francesco Cricri, Jani Lainema, Emre Aksu, Miska Hannuksela
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible.
no code implementations • 3 May 2019 • Caglar Aytekin, Francesco Cricri, Emre Aksu
In this paper we apply a compressibility loss that enables learning highly compressible neural network weights.
no code implementations • 28 May 2018 • Caglar Aytekin, Xingyang Ni, Francesco Cricri, Jani Lainema, Emre Aksu, Miska Hannuksela
In this work, we propose an end-to-end block-based auto-encoder system for image compression.
no code implementations • 8 Feb 2018 • Caglar Aytekin, Francesco Cricri, Emre Aksu
In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses.
no code implementations • 1 Feb 2018 • Caglar Aytekin, Xingyang Ni, Francesco Cricri, Emre Aksu
We show the effect of l2 normalization on anomaly detection accuracy.
no code implementations • 24 Jan 2018 • Caglar Aytekin, Francesco Cricri, Lixin Fan, Emre Aksu
In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem.
no code implementations • 27 Dec 2017 • Caglar Aytekin, Xingyang Ni, Francesco Cricri, Lixin Fan, Emre Aksu
By using these encoded images, we train a memory-efficient network using only 0. 048\% of the number of parameters that other deep salient object detection networks have.
1 code implementation • 6 Dec 2016 • Francesco Cricri, Xingyang Ni, Mikko Honkala, Emre Aksu, Moncef Gabbouj
Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder.