James D. Johnston's scientific contributions
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Publications (6)
The notion of perceptual coding, which is based on the concept of
distortion masking by the signal being compressed, is developed.
Progress in this field as a result of advances in classical coding
theory, modeling of human perception, and digital signal processing, is
described. It is proposed that fundamental limits in the science can be
expresse...
The problem of image compression is to achieve a low bit rate in the digital representation of an input image or video signal with minimum perceived loss of picture quality. Since the ultimate criterion of quality is that judged or measured by the human receiver, it is important that the compression (or coding) algorithm minimizes perceptually mean...
In this paper we present a sub-band coder for true color images that uses an
empirically derived perceptual masking model to set the allowable quantization noiselevel
not only for each sub-band but also for each pixel in a given sub-band. The input
image is converted into YIQ space and each channel is passed through a separable
Generalized Quadratu...
The authors present a 16-band subband coder arranged as four
equal-width subbands in each dimension, It uses an empirically derived
perceptual masking model, to set noise-level targets not only for each
subband but also for each pixel in a given subband. The noise-level
target is used to set the quantization levels in a DPCM (differential
pulse cod...
The authors present a 16-band subband coder arranged as four equal-width subbands in each dimension, It uses an empirically derived perceptual masking model to set noise-level targets not only for each subband but also for each pixel in a given subband. The noise-level target is used to set the quantization levels in a DPCM (differential pulse code...
Citations
... Some JND models measure the visibility threshold in fre- quency domain such as sub-band [12]- [14], discrete cosine transform(DCT) [2]- [5] and wavelet domains [15]- [18]. ...
Reference: JND modeling: Approaches and applications
... Contrast encoding is fundamental in development of image quality measures based on the study of the early stages of the human visual system (HVS) [5]. There were proposals that models of image quality should aim not to measure the quantity of image transmitted, but to discriminate visible differences between 'ideal'/original and degraded images, bringing image quality measurement closer to contemporary contrast psychophysics [13,20]. These newer models incorporated ideas inspired by the multi-scale spatial transform performed by the visual system, and began to acknowledge the intrinsic, efficient connection between image statistics and visual/neural encoding [2,7]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/428773259649025-1479238919166_Q64/Ruth-Rosenholtz.jpg)
... In this paper, according to the declared focus on modeling the mechanisms of visual system, the second approach is taken as the basisthe synthesis of optimal quantization methods, or, as it is commonly referred to in the field of video / image processing, the synthesis of perceptual coding [24]. The main difference of perceptual coding from encoders reducing redundancy in images is the choice of the similarity metric for the encoded and decoded images [25]. ...
... Based on the knowledge of the known objects, knowledge and semantic-based coding methods were developed, such as parameterized modeling for the facial animation [71][72][73][74][75][76]. Modeling the scene or image content directly is difficult and restricted in wild scenarios; in contrast, perceptual coding [77][78][79][80][81][82][83][84][85][86] attempts to incorporate the vision model into the coder by using the knowledge of HVS [87]. In [87], a nonlinear mathematical HVS model was proposed for image compression, which was developed from the psycho-visual and physiological characteristics of the HVS, and a reduced achromatic model was developed as a nonlinear filter fol- lowed by a bandpass spatial filter. ...
... In order to address the difficulty identified in the previous paragraph, we develop an information-theoretic framework for model reduction. Very much like MP3 compression is about retaining information that matters most to the human ear (27), model reduction is about keeping information that matters most to predict the future (28,29). Inspired by this simple insight, we formalize model reduction as a lossy compression problem known as the information bottleneck (IB) (30? , 31). ...
