Improving JPEG performance in conjunction with cloud editing for remote sensing applications
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The authors propose an improved version of JPEG coding for compressing remote sensing images obtained by optical sensors onboard microsatellites. The approach involves expanding cloud features to include their cloud-land transitions, thereby simplifying their coding and subsequent compression. The system is fully automatic and appropriate for onboard implementation. Its improvement in coding stems from the realization that a large number of bits are used for coding the blocks that contain the transition regions between bright clouds, if present in the image, and the dark background. A fully automatic cloud-segmentation algorithm is therefore used to identify the external boundaries of the clouds, then smooth the corresponding blocks prior to coding. Further gains are also achieved by modifying the quantization table used for coding the coefficients of the discrete cosine transform. Compared to standard JPEG, at the same level of reconstruction quality, the new method can achieve compression ratio improvement by 13-161%, depending upon the context and the amount of cloud present in the specific image. The results are demonstrated with the help of several real images obtained by the University of Surrey, U.K., satellites.Keywords:
JPEG
In this paper, we propose a statistical test to discriminate between original and forged regions in JPEG images, under the hypothesis that the former are doubly compressed while the latter are singly compressed. New probability models for the DCT coefficients of singly and doubly compressed regions are proposed, together with a reliable method for estimating the primary quantization factor in the case of double compression. Based on such models, the probability for each DCT block to be forged is derived. Experimental results demonstrate a better discriminating behavior with respect to previously proposed methods.
JPEG
Lossless JPEG
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We describe the RD-OPT algorithm for DCT quantization optimization, which can be used as an efficient tool for near-optimal rate control in DCT-based compression techniques, such as JPEG and MPEG. RD-OPT measures DCT coefficient statistics for the given image data to construct rate/distortion-specific quantization tables with nearly optimal tradeoffs.
Trellis quantization
JPEG
Rate–distortion theory
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Citations (42)
The Discrete Cosine Transform (DCT) is widely used in lossy image and video compression schemes such as JPEG and MPEG. In this paper we describe RD-OPT, an efficient algorithm for constructing DCT quantization tables with optimal rate-distortion tradeoffs for a given image. The algorithm uses DCT coefficient distribution statistics in a novel way and uses a dynamic programming strategy to produce optimal quantization tables over a wide range of rates and distortions. It can be used to compress images at any desired signal-to-noise ratio or compressed size.
JPEG
Trellis quantization
Lossy compression
Rate–distortion theory
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Citations (44)
The compacting processes of M-JPEG and MPEGⅡ are introduced. The procedures of the discrete cosine transform, quantization, moving compensating prediction technology and compressing code etc. are discussed. The deficiency and superiority of M-JPEG and MPEGⅡ IBP are analyzed.
JPEG
Code (set theory)
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Amount and size of remote sensing (RS) images acquired by modern systems are so large that data have to be compressed in order to transfer, save and disseminate them. Lossy compression becomes more popular for aforementioned situations. But lossy compression has to be applied carefully with providing acceptable level of introduced distortions not to lose valuable information contained in data. Then introduced losses have to be controlled and predicted and this is problematic for many coders. In this paper, we analyze possibilities of predicting mean square error or, equivalently, PSNR for coders based on discrete cosine transform (DCT) applied either for compressing singlechannel RS images or multichannel data in component-wise manner. The proposed approach is based on direct dependence between distortions introduced due to DCT coefficient quantization and losses in compressed data. One more innovation deals with possibility to employ a limited number (percentage) of blocks for which DCT-coefficients have to be calculated. This accelerates prediction and makes it considerably faster than compression itself. There are two other advantages of the proposed approach. First, it is applicable for both uniform and non-uniform quantization of DCT coefficients. Second, the approach is quite general since it works for several analyzed DCT-based coders. The simulation results are obtained for standard test images and then verified for real-life RS data.
Lossy compression
Trellis quantization
Data compression ratio
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JPEG has been widely used so far.As a generic compression algorithm for the static images,JPEG is too complex to be suitable under some circumstances.Based on the analysis of the principles of JPEG,by improving the quantization module and the coding module of JPEG,a simple compression algorithm based on discrete Cosine Transform(DCT) is proposed.The proposed algorithm shows good compression performance for low compression ratio applications,the compression ratio is lower than 15,while it is much simpler than JPEG.
JPEG
Lossless JPEG
Data compression ratio
Lossy compression
JPEG 2000
Color Cell Compression
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A method for prediction and providing compressed image quality for lossy compression techniques based on discrete cosine transform (DCT) is proposed. A specific property of the designed method is its ability to predict compressed image quality with appropriately high accuracy using a limited number of analyzed blocks. This accelerates prediction of lossy compression quality substantially. The method is originally proposed for JPEG with uniform quantization and then generalized for other, advanced, DCT based coders AGU and ADCT.
Lossy compression
JPEG
Trellis quantization
Lossless JPEG
Compression artifact
Texture compression
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In lossy image compression schemes utilizing the discrete cosine transform (DCT), quantization of the DCT coefficients introduces error in the image representation and a loss of signal information. At high compression ratios, this introduced error produces visually undesirable compression artifacts that can dramatically lower the perceived quality of a particular image. This paper provides a spatial domain model of the quantization error based on a statistical noise model of the error introduced when quantizing the DCT coefficients. The resulting theoretically derived spatial domain quantization noise model shows that, in general, the compression noise in the spatial domain is both correlated and spatially varying. This provides some justification for many of the ad hoc artifact removal filters that have been proposed. An accurate description of quantization noise is essential if one hopes to remove, or at least alleviate, the visibility of compression artifacts.
Trellis quantization
Lossy compression
Compression artifact
JPEG
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A task of alternative (faster and more efficient in compression ratio sense) coding of discrete cosine transform (DCT) coefficients within JPEG based image compression approach is considered. In the data processing chain, it is proposed to apply a recursive group coding (RGC) as an alternative to arithmetic or Huffmann coding. In contrast to the aforementioned data coding techniques, the RGC method is able to efficiently code symbols of very large alphabets (each block of 8x8 pixels of quantized DCT coefficients can be represented as such 64-byte or 128-byte symbol). Comparative analysis of efficiency for the standard JPEG and its proposed modification (for three images of three different digital cameras) is carried out using six different quantization tables. It is shown that RCG possesses low computational complexity and a high speed of compression simultaneously with higher compression ratio (CR) compared to the standard JPEG. The benefit in CR appears to be larger for smaller quantization steps (QSs) that mainly correspond to SHQ (super high quality) mode. This benefit can reach up to 10%. It is also demonstrated that the benefits exists for uniform quantization tables. The proposed coding method can be used for an additional compression of JPEG-images in coding traffic of communication lines. For this purpose, data of JPEG-images have to be partly decoded (till the level of the quantized DCT coefficients) and then recoded by RGC.
JPEG
Lossless JPEG
JPEG 2000
Arithmetic coding
Color Cell Compression
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