HEVC still image coding and high efficiency image file format

Lainema, Jani; Hannuksela, Miska M.; Vadakital, Vinod Kumar Malamal; Aksu, Emre

doi:10.1109/icip.2016.7532321

Cited by 29 publications

(16 citation statements)

References 11 publications

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“…As a 30-years-old codec, JPEG merely applies predictive coding on DC coefficients after DCT transform, and the coding gain mainly comes from consecutive quantized zero coefficients for RLE. There is no intra-prediction coding tool like HEVC [6] in JPEG, so the optimization at low bitrate becomes a zero-sum game of size or quality. For low bitrate JPEG optimization, there are alternative research works being proposed to either preediting [15] or post-processing with artifacts correction [17] to maintain good visual quality at reduced bitrate.…”

Section: Discussionmentioning

confidence: 99%

“…As of today, JPEG [3] remains the most used lossy image compression standard for digital images in content sharing and various image capture devices [1] [2]. Since JPEG was developed in 1992, new image coding standards such as JPEG 2000 [4], WebP [5], and HEIF [6] have been proposed to respond to the continual expansion of multimedia applications. The JPEG 2000 adopts the Discrete Wavelet Transform (DWT) and arithmetic coding with a layered file format that offers flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…Since 2017, Apple switches to use HEIF as the default photo capturing format in iPhone [13]. We expect more mobile phone manufacturers to follow, and the digital imaging world is one step closer to replace JPEG with the state-of-the-art HEVC [6] coded image format. However, we expect JPEG to stay for at least a few more decades [2] because JPEG still satisfies average user demands.…”

Section: Introductionmentioning

confidence: 99%

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Optimal JPEG Quantization Table Fusion by Optimizing Texture Mosaic Images and Predicting Textures

Huang

2021

Preprint

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The JPEG standard allows the use of a customized quantization table; however, it is still challenging to find an optimal quantization table timely. This work aims to solve the dilemma of balancing computational cost and image-specific optimality by introducing a new concept of texture mosaic images. Instead of optimizing a single image or a collection of representative images, the conventional JPEG optimization techniques can be applied to the texture mosaic image to obtain an optimal quantization table for each texture category. We use the simulated annealing technique as an example to validate our framework. To effectively learn the visual features of textures, we use the ImageNet pre-trained MobileNetV2 model to train and predict the new image's texture distribution, then fuse optimal texture tables to come out with an image-specific optimal quantization table. Our experiment demonstrates around 30% size reduction with a slight decrease of FSIM quality but visually indistinguishable on the evaluation datasets. Moreover, our rate-distortion curve shows superior and competitive performance against other prior works under a high-quality setting. The proposed method, denoted as JQF, achieves per image optimality for JPEG encoding with less than one second additional timing cost.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal JPEG Quantization Table Fusion by Optimizing Texture Mosaic Images and Predicting Textures

Huang

2021

Preprint

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“…Social media platforms have also led to an explosion in the amount of image data being shared and stored online. Handling such large volumes of image and video data is inconceivable without the use of compression algorithms such as JPEG [2] [3], AVIF (AV1 Intra) [4] [5], HEIF (HEVC Intra) [6], H.264 [7] [8], HEVC [9], EVC [10], VP9 [11], AV1 [12], SVT-AV1 [13], and the upcoming VVC and AV2 standards.…”

Section: Introductionmentioning

confidence: 99%

A Hitchhiker's Guide to Structural Similarity

Venkataramanan,

Wu,

Bovik

et al. 2021

Preprint

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The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms. Several public implementations of the SSIM and Multiscale-SSIM (MS-SSIM) algorithms have been developed, which differ in efficiency and performance. This "bendable ruler" makes the process of quality assessment of encoding algorithms unreliable. To address this situation, we studied and compared the functions and performances of popular and widely used implementations of SSIM, and we also considered a variety of design choices. Based on our studies and experiments, we have arrived at a collection of recommendations on how to use SSIM most effectively, including ways to reduce its computational burden.

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“…Comparison studies presented in [4] have shown it to be 25-34% more efficient for the same SSIM value. The High Efficiency Image File Format (HEIF) is a standard that supports the storage of image data encoded using the HEVC standard and is shown to provide up to 25% reduction in bitrate compared to JPEG 2000 for the same objective quality [5]. Similar to HEIF, AVIF, which is the latest image compression standard, allows encapsulating AV1 intra-frame coded content and supports High Dynamic Range (HDR) and Wide Color Gamut (WCG) images as well as Standard Dynamic Range (SDR) [6].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Next-Generation Image Coding Standard AVIF

Barman

Martini

2020

2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)

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This paper presents a comparative performance evaluation of the newly proposed AV1 Image File Format (AVIF) vs. other state-of-the art image codecs, for natural, synthetic and gaming images. The codecs are compared in terms of Rate-quality curves and BD-Rate savings considering different quality metrics. AVIF results in the best overall performance considering both 4:2:0 and 4:4:4 chroma subsampling encoded images for all types of images.

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HEVC still image coding and high efficiency image file format

Cited by 29 publications

References 11 publications

Optimal JPEG Quantization Table Fusion by Optimizing Texture Mosaic Images and Predicting Textures

Optimal JPEG Quantization Table Fusion by Optimizing Texture Mosaic Images and Predicting Textures

A Hitchhiker's Guide to Structural Similarity

An Evaluation of the Next-Generation Image Coding Standard AVIF

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