Paper Reading AI Learner

Two-Layer Lossless HDR Coding using Histogram Packing Technique with Backward Compatibility to JPEG

2018-08-02 03:02:48
Osamu Watanabe, Hiroyuki Kobayashi, Hitoshi Kiya

Abstract

An efficient two-layer coding method using the histogram packing technique with the backward compatibility to the legacy JPEG is proposed in this paper. The JPEG XT, which is the international standard to compress HDR images, adopts two-layer coding scheme for backward compatibility to the legacy JPEG. However, this two-layer coding structure does not give better lossless performance than the other existing methods for HDR image compression with single-layer structure. Moreover, the lossless compression of the JPEG XT has a problem on determination of the coding parameters; The lossless performance is affected by the input images and/or the parameter values. That is, finding appropriate combination of the values is necessary to achieve good lossless performance. It is firstly pointed out that the histogram packing technique considering the histogram sparseness of HDR images is able to improve the performance of lossless compression. Then, a novel two-layer coding with the histogram packing technique and an additional lossless encoder is proposed. The experimental results demonstrate that not only the proposed method has a better lossless compression performance than that of the JPEG XT, but also there is no need to determine image-dependent parameter values for good compression performance without losing the backward compatibility to the well known legacy JPEG standard.

Abstract (translated)

本文提出了一种使用直方图打包技术的高效双层编码方法,该方法具有与传统JPEG的向后兼容性。 JPEG XT是压缩HDR图像的国际标准,采用双层编码方案向后兼容传统JPEG。然而,与具有单层结构的HDR图像压缩的其他现有方法相比,这种双层编码结构不能提供更好的无损性能。此外,JPEG XT的无损压缩在确定编码参数方面存在问题;无损性能受输入图像和/或参数值的影响。也就是说,找到值的适当组合对于实现良好的无损性能是必要的。首先指出考虑HDR图像直方图稀疏性的直方图打包技术能够提高无损压缩的性能。然后,提出了一种采用直方图打包技术和附加无损编码器的新型双层编码。实验结果表明,不仅所提出的方法具有比JPEG XT更好的无损压缩性能,而且不需要确定与图像相关的参数值以获得良好的压缩性能,同时不会失去与众所周知的传统的向后兼容性。 JPEG标准。

URL

https://arxiv.org/abs/1808.00956

PDF

https://arxiv.org/pdf/1808.00956.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot