Jani Lainema
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View article: L2C -- Learning to Learn to Compress
L2C -- Learning to Learn to Compress Open
In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural network for generalization performance. However, at inference tim…
View article: Adaptation and Attention for Neural Video Coding
Adaptation and Attention for Neural Video Coding Open
Neural image coding represents now the state-of-the-art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-to-end learned video codec that introduces several architec…
View article: Transform Coding in the VVC Standard
Transform Coding in the VVC Standard Open
In the past decade, the development of transform coding techniques has achieved significant progress and several advanced transform tools have been adopted in the new generation Versatile Video Coding (VVC) standard. In this paper, a brief…
View article: Quantization and Entropy Coding in the Versatile Video Coding (VVC) Standard
Quantization and Entropy Coding in the Versatile Video Coding (VVC) Standard Open
The paper provides an overview of the quantization and entropy coding methods in the Versatile Video Coding (VVC) standard. Special focus is laid on techniques that improve coding efficiency relative to the methods included in the High Eff…
View article: Efficient Adaptation of Neural Network Filter for Video Compression
Efficient Adaptation of Neural Network Filter for Video Compression Open
We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines. The fine-tuning is performed at encoder side to adapt the neural network to th…
View article: Learning to Learn to Compress
Learning to Learn to Compress Open
In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural network for generalization performance. However, at inference tim…
View article: End-to-End Learning for Video Frame Compression with Self-Attention
End-to-End Learning for Video Frame Compression with Self-Attention Open
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. In this paper, we propose an end-to-end learned system for com…
View article: General Video Coding Technology in Responses to the Joint Call for Proposals on Video Compression With Capability Beyond HEVC
General Video Coding Technology in Responses to the Joint Call for Proposals on Video Compression With Capability Beyond HEVC Open
After the development of the High-Efficiency Video Coding Standard (HEVC), ITU-T VCEG and ISO/IEC MPEG formed the Joint Video Exploration Team (JVET), which started exploring video coding technology with higher coding efficiency, including…
View article: A Compression Objective and a Cycle Loss for Neural Image Compression
A Compression Objective and a Cycle Loss for Neural Image Compression Open
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss. These terms are applied on the encoder output of an autoencoder and are used in combination with reconstruction losse…
View article: Compressing Weight-updates for Image Artifacts Removal Neural Networks
Compressing Weight-updates for Image Artifacts Removal Neural Networks Open
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. At encoder side, we fine-tune a pre-trained artifact remova…
View article: Compressing Weight-updates for Image Artifacts Removal Neural Networks
Compressing Weight-updates for Image Artifacts Removal Neural Networks Open
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. At encoder side, we fine-tune a pre-trained artifact remova…
View article: A Compression Objective and a Cycle Loss for Neural Image Compression
A Compression Objective and a Cycle Loss for Neural Image Compression Open
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss. These terms are applied on the encoder output of an autoencoder and are used in combination with reconstruction losse…
View article: Block-optimized Variable Bit Rate Neural Image Compression
Block-optimized Variable Bit Rate Neural Image Compression Open
In this work, we propose an end-to-end block-based auto-encoder system for image compression. We introduce novel contributions to neural-network based image compression, mainly in achieving binarization simulation, variable bit rates with …
View article: Mvc: Advanced Low Bit Rate Codec For Mobile Multimedia
Mvc: Advanced Low Bit Rate Codec For Mobile Multimedia Open
Publication in the conference proceedings of EUSIPCO, Tampere, Finland, 2000