Low complexity perceptual image coding by just-noticeable difference model based adaptive downsampling

Wang, Zhe; Baroud, Yousef; Najmabadi, Seyyed Mahdi; Simon, Sven

doi:10.1109/pcs.2016.7906359

Cited by 3 publications

(14 citation statements)

References 11 publications

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“…It is obvious that the value ofĈ MAD is 81 times as large as C MAD . Therefore, instead of comparing C MAD to the threshold T C as in Equation (14), the modifiedĈ MAD is now compared to the new threshold T hc = 81 • T C . This strategy indeed requires extra hardware resources if T C is not implemented as a constant but can guarantee the accuracy of CS without using floating-point operation.…”

Section: High Contrast Activitymentioning

confidence: 99%

“…Generate the skewed pixel processing order described in [14]. • Downsample the current 2 × 2 input block.…”

Section: Top-level Architecture Of the Jnd-based Pixel-domain Perceptmentioning

confidence: 99%

“…In this work, a new region-adaptive pixel-domain JND model based on efficient local operations is proposed for a more accurate detection of visibility thresholds compared with the classic JND model [9] and for a reduced complexity compared with more recent ones [11,12]. A low complexity pixel-domain perceptual image coder [14] is then used to exploit the visibility thresholds given by the proposed JND model. The coding algorithm addresses both coding efficiency and visual quality issues in conventional pixel-domain coders in a framework of adaptive downsampling guided by perceptual regions-of-interest (ROIs) based on JND thresholds.…”

Section: Introductionmentioning

confidence: 99%

“…For clarity, the JND module, as well as the delay element for synchronizing the JND module outputs with the input pixel stream for the encoder, is omitted from the discussions on the encoder architecture in the rest of the paper. In addition, since existing works (e.g., [26]) have well covered architectural aspects of fundamental pixel-domain predictive coding algorithms such as JPEG-LS, the following discussion focuses mainly on the aspects of the proposed encoder architecture that enable the skewed pixel processing, the JND-based adaptive downsampling and the ROI-based pixel selection [14]. Figure 17.…”

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confidence: 99%

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A JND-Based Pixel-Domain Algorithm and Hardware Architecture for Perceptual Image Coding

et al. 2019

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This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual image coding architecture, which is based on downsampling and predictive coding. The downsampling is performed adaptively on the input image based on regions-of-interest (ROIs) identified by measuring the downsampling distortions against the visibility thresholds given by the JND model. The coding error at any pixel location can be guaranteed to be within the corresponding JND threshold in order to obtain excellent visual quality. Experimental results show the improved accuracy of the proposed JND model in estimating visual redundancies compared with classic JND models published earlier. Compression experiments demonstrate improved rate-distortion performance and visual quality over JPEG-LS as well as reduced compressed bit rates compared with other standard codecs such as JPEG 2000 at the same peak signal-to-perceptible-noise ratio (PSPNR). FPGA synthesis results targeting a mid-range device show very moderate hardware resource requirements and over 100 Megapixel/s throughput of both the JND model and the perceptual encoder.

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Section: High Contrast Activitymentioning

confidence: 99%

“…Generate the skewed pixel processing order described in [14]. • Downsample the current 2 × 2 input block.…”

Section: Top-level Architecture Of the Jnd-based Pixel-domain Perceptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A JND-Based Pixel-Domain Algorithm and Hardware Architecture for Perceptual Image Coding

et al. 2019

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“…Therefore, multimedia and computer vision applications can really benefit from automatic tools that are capable of assessing image quality. More specifically, image quality assessment (IQA) methods can be used, for example, to determine optimal codec parameters [1], find best perceptual coding schemes [2][3][4][5][6], and design efficient image watermarking algorithms [7,8]. Moreover, a recent report by Conviva R shows that viewers are demanding a higher quality of delivered multimedia content [9].…”

Section: Introductionmentioning

confidence: 99%

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

et al. 2018

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Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.The filters W i are learned via independent component analysis (ICA). The binarized features b i are aggregated following the same procedure described for generating the LBP labels. The descriptive features are obtained by computing the histogram of the aggregated data.Similarly to the LBP, which generates LBP channels, the BSIF generates coded images. These coded images are the set of labels generated after the binarized features are computed using Equation (17) and aggregated using Equation (1). The aggregation of BSIF results is based on a selected number of bits, instead of the number of neighbors of the labeled pixel. The labeling depends on the relationship between the patch size l and the number of binarized features b i . Figure 6 shows the BSIF coded images corresponding to the same reference using different BSIF parameters. As can be seen in this figure, the textured information depends on the patch size l and on the number of bits. The number of bits is less or equal l 2 − 1. This is the reason why the second column does not contain BSIF coded images for 9, 10, 11, or 12 bits. Figure 6 shows that the choice of the number of bits and patch sizes is important for texture analysis algorithms. Therefore, multiscale approaches that incorporate several combinations of these parameters are interesting [74][75][76][77].

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Elastic Downsampling: An Adaptive Downsampling Technique to Preserve Image Quality

et al. 2021

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This paper presents a new adaptive downsampling technique called elastic downsampling, which enables high compression rates while preserving the image quality. Adaptive downsampling techniques are based on the idea that image tiles can use different sampling rates depending on the amount of information conveyed by each block. However, current approaches suffer from blocking effects and artifacts that hinder the user experience. To bridge this gap, elastic downsampling relies on a Perceptual Relevance analysis that assigns sampling rates to the corners of blocks. The novel metric used for this analysis is based on the luminance fluctuations of an image region. This allows a gradual transition of the sampling rate within tiles, both horizontally and vertically. As a result, the block artifacts are removed and fine details are preserved. Experimental results (using the Kodak and USC Miscelanea image datasets) show a PSNR improvement of up to 15 dB and a superior SSIM (Structural Similarity) when compared with other techniques. More importantly, the algorithms involved are computationally cheap, so it is feasible to implement them in low-cost devices. The proposed technique has been successfully implemented using graphics processors (GPU) and low-power embedded systems (Raspberry Pi) as target platforms.

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Low complexity perceptual image coding by just-noticeable difference model based adaptive downsampling

Cited by 3 publications

References 11 publications

A JND-Based Pixel-Domain Algorithm and Hardware Architecture for Perceptual Image Coding

A JND-Based Pixel-Domain Algorithm and Hardware Architecture for Perceptual Image Coding

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

Elastic Downsampling: An Adaptive Downsampling Technique to Preserve Image Quality

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