Blocky effect removal device for compression coded image

Abstract

The invention discloses a device for removing blocky effect possible to be produced at boundary of image after decoding as DCT coefficient is lost caused by quantification in image compression process. The device comprises a region classification unit, a filtering processing unit and a selector; firstly the region classification unit is used to detect block boundary region and mark the block boundary region as smooth gradient type, brightness phase step type and complex texture type, and then the selector selects a filter with corresponding smooth strength according to the output signal of the region classification unit to output or directly outputs the decoded image signal. The invention filters boundary regions in different types in different strength in a smoothing way, can effectively remove the blocky effect in the image and meanwhile detail section or high frequency components can be retained; in addition, the invention is non-iterative operation, the whole processing procedure can be completed by integer arithmetic and shift operation, the blocky effect can be removed in real time, and complexity of hardware realization is low while removal efficiency is higher and effect is better.

Description

technical field [0001] The invention relates to a device for removing block effect of compressed and encoded images, in particular to a device for removing DCT coefficients lost due to quantization in the process of image compression, and the decoded image may appear discontinuous at the block boundary. Block effect device. Background technique [0002] At present, block-based discrete cosine transform (Discrete Cosine Transform DCT) compression technology has been widely used in the compression coding of still images and dynamic images, such as JPEG (Joint Photographic Experts Group, Joint Photographic Experts Group), MPEG (Moving Pictures Experts Group, Dynamic Picture Experts Group), H.261 and H.263, etc. The compression process includes block discrete cosine transform, quantized DCT coefficients, entropy coding, etc. Due to the loss of DCT coefficients due to quantization, the decoded image may appear discontinuous at the block boundary, that is, block effect occurs. I...

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Problems solved by technology

The compression process includes block discrete cosine transform, quantized DCT coefficients, entropy coding, etc. Due to the loss of DCT coefficients due to quantization, the decoded image may appear discontinuous at the block boundary, that is, block effect occurs. Especially when the compression rate is high, it seriously affects the visual effect of the image

[0003] Existing methods for removing blockiness after decoding can be divided into iterative methods and non-iterative methods according to the processing process: in the iterative method, various prior knowledge of the smooth original image are integrated based on the method of projections onto Convex Sets POCS. It is defined as a closed convex set, and the decoded image is projected onto the intersection of these closed convex sets, and then the processed image is as close as possible to the original image through repeated iterations. The Maximum a Posteriori MAP (Maximum a Posteriori MAP) method is also an iterative method. It uses the processed image to meet the requirements of smoothness and other characteristics to determine the probability function of the image, so as to obtain the post-processed image that meets the maximum posteriori probability. Although the processing effect of the method is good, but the amount of calculation is large, it is difficult to use in practical applications; the non-iterative method has a spatial domain filtering method, because the discontinuity of the block boundary is a step jump, which is a block boundary in the frequency domain Contains some high-frequency components, so block effects are generated, so low-pass filters can be used to low-pass filter the pixels on the block boundary to filter out high-frequency components, thereby removing block effects of the image. This method is simple, but easy to lose The details of the image, causing the image to blur

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