FLIF: Free lossless image format based on MANIAC compression

Sneyers, Jon; Wuille, Pieter

doi:10.1109/icip.2016.7532320

Cited by 133 publications

(92 citation statements)

References 1 publication

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“…The overall framework of the DSSLIC codec is shown in In this paper, both s and c are losslessly encoded using the FLIF codec [13], which is a state-of-the-art lossless image codec.…”

Section: Dsslic Codec Frameworkmentioning

confidence: 99%

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression

Akbari

Liang

Han

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Deep learning has revolutionized many computer vision fields in the last few years, including learning-based image compression. In this paper, we propose a deep semantic segmentation-based layered image compression (DSSLIC) framework in which the semantic segmentation map of the input image is obtained and encoded as the base layer of the bit-stream. A compact representation of the input image is also generated and encoded as the first enhancement layer. The segmentation map and the compact version of the image are then employed to obtain a coarse reconstruction of the image. The residual between the input and the coarse reconstruction is additionally encoded as another enhancement layer. Experimental results show that the proposed framework outperforms the H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics across a wide range of bit rates in RGB domain. Besides, since semantic segmentation map is included in the bit-stream, the proposed scheme can facilitate many other tasks such as image search and object-based adaptive image compression 1 .

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“…The overall framework of the DSSLIC codec is shown in In this paper, both s and c are losslessly encoded using the FLIF codec [13], which is a state-of-the-art lossless image codec.…”

Section: Dsslic Codec Frameworkmentioning

confidence: 99%

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression

Akbari

Liang

Han

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…(i) 29.9 improvement compared to CALIC [2], a traditional lossless image codec; (ii) 12.5 improvement compared to our previous method [6]; (iii) 9.1 improvement compared to FLIF [3], the current state-of-the-art lossless image codec; and (iv) 3 improvement compared to REP-CNN [5].…”

Section: B) Lossless Compression Resultsmentioning

confidence: 92%

Deep-learning-based macro-pixel synthesis and lossless coding of light field images

Schiopu

Munteanu

2019

SIP

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Deep-learning-based macro-pixel synthesis and lossless coding of light field imagesionut schiopu and adrian munteanu This paper proposes a novel approach for lossless coding of light field (LF) images based on a macro-pixel (MP) synthesis technique which synthesizes the entire LF image in one step. The reference views used in the synthesis process are selected based on four different view configurations and define the reference LF image. This image is stored as an array of reference MPs which collect one pixel from each reference view, being losslessly encoded as a base layer. A first contribution focuses on a novel network design for view synthesis which synthesizes the entire LF image as an array of synthesized MPs. A second contribution proposes a network model for coding which computes the MP prediction used for lossless encoding of the remaining views as an enhancement layer. Synthesis results show an average distortion of 29.82 dB based on four reference views and up to 36.19 dB based on 25 reference views. Compression results show an average improvement of 29.9 over the traditional lossless image codecs and 9.1 over the state-of-the-art.

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“…Throughout the years they have given me endless support, and have always believed in me more than I believed in myself. CALIC (1996) [1], JPEG-LS (1999) [2], Lossless JPEG2000 (2000) [3], GLICBAWLS (2001) [4], and FLIF (2015) [5].…”

Section: Approval By Thesis Directormentioning

confidence: 99%

Lossless Image Compression Using Reversible Integer Wavelet Transforms and Convolutional Neural Networks

Ahanonu¹,

Marcellin²,

Bilgin

2018

2018 Data Compression Conference

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LIST OF FIGURES-Continued 4.12 Distributions of entropy reduction over the natural dataset for subbnads in the 1st decomposition level. Solid and dashed lines represent networks trained with L1 and L2 loss, respectively.. .. .. .. .. . 4.13 Distributions of entropy reduction over the natural dataset for subbnads in the 2nd decomposition level. Solid and dashed lines represent networks trained with L1 and L2 loss, respectively.. .. .. .. .. . 4.14 Distributions of entropy reduction over the pathology dataset for subbnads in the 1st decomposition level. Solid and dashed lines represent networks trained with L1 and L2 loss, respectively.. .. .. .. 4.15 Distributions of entropy reduction over the pathology dataset for subbnads in the 2nd decomposition level. Solid and dashed lines represent networks trained with L1 and L2 loss, respectively.. .. .. 4.16 Distributions of entropy reduction over the graphics dataset for subbnads in the 1st decomposition level. Solid and dashed lines represent networks trained with L1 and L2 loss, respectively.. .. .. .. .. . 4.17 Distributions of entropy reduction over the graphics dataset for subbnads in the 2nd decomposition level. Solid and dashed lines represent networks trained with L1 and L2 loss, respectively.. .. .. .. .. . 4.18 Example prediction from the natural dataset of subbands within D 1. The entropy of the original coefficients, and prediction residuals, are

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FLIF: Free lossless image format based on MANIAC compression

Cited by 133 publications

References 1 publication

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression

Deep-learning-based macro-pixel synthesis and lossless coding of light field images

Lossless Image Compression Using Reversible Integer Wavelet Transforms and Convolutional Neural Networks

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