![]() Perceptual quality: A measure of the perceptual impact of the unwanted signal and how it detracts from the enjoyment or interpretation of an image or video. Mathematical distortion: An objective measure of difference between the original acquired content and the processed version. Noise can be defined from different perspectives: These include sensor noise introduced during content acquisition, artifacts caused by compression during coding, transmission errors due to noisy communication channels, and sample aliasing due to resolution adaptation (if applied). Noise or artifacts are introduced into image and video content throughout the whole processing workflow. The outcome of this is that mean squared error (MSE)-based metrics are still the most commonly used assessment methods, both for in-loop optimization and for external performance comparisons. More complex, perceptually inspired metrics, although improving significantly in recent years, can still be inconsistent under certain test conditions. The main issue here is that simple metrics bear little relevance to the HVS and generally do not correlate well with subjective results, especially at lower bit rates when distortions are higher. Objectively: Using metrics that attempt to capture the perceptual mechanisms of the human visual system (HVS). They are costly and time consuming, but generally effective. Subjective testing conditions must be closely controlled, with appropriate screening of observers and postprocessing of the results to ensure consistency and statistical significance. Subjectively: Requiring many observers and many presentations of a representative range of impairment conditions and content types. It can be achieved in one of two ways: 1. The relationship between the CLS-iteration adaptive successive approximations solution and the hierarchical Bayesian solution discussed in the previous section is also applicable here.įirstly it is worth stating that assessing the quality of impaired image or video content, whether due to transmission losses or compression artifacts, is not straightforward. Expressions for the iterative evaluation of the unknown parameters and the reconstructed image are derived. The evidence analysis within the hierarchical Bayesian paradigm, mentioned above, is applied to the same problem in. The second approach is based on the theory of projections onto convex sets (POCS), which has found applications in a number of recovery problems. The first one is based on the CLS formulation and a successive approximations iteration is utilized for obtaining the solution. Two solutions are developed for the removal of blocking artifacts in still images. For example, in the problem of removing the blocking artifacts is formulated as a recovery problem, according to which an estimate of the blocking artifact-free original image is estimated by utilizing the available quantized data, knowledge about the quantizer step size, and prior knowledge about the smoothness of the original image.Ī deterministic formulation of the problem is followed in. A number of techniques have been developed for removing such blocking artifacts for both still images and video. As a result of this processing, annoying blocking artifacts result, primarily at high compression ratios. The Discrete Cosine Transform (DCT) of such blocks (representing either the image intensity when dealing with still images or intracoding of video blocks or frames, or the displaced frame difference when dealing with intercoding of video blocks or frames) is taken and the resulting DCT coefficients are quantized. More specifically, in the majority of existing image and video compression algorithms the image (or frame in an image sequence) is divided into square blocks which are processed independently from each other. The problem of removing compression artifacts addresses the recovery of information lost due to the quantization of parameters during compression. Chun-JenTsai, in The Essential Guide to Image Processing, 2009 15.6.4.1 Removal of Compression Artifacts ![]()
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