#Super png compressor free#

Basically, you have a few solutions to reduce the size of an image but most of the time it will decrease the quality too.

#Super png compressor free#

What does this tool do? Compress PNG/JPG is an online free tool which aim is to help you compress your image (jpeg or png). dollar UAH Ukraine Hryvnia VND Vietnamese Dong ZAR South African rand S$ Singapore dollar SEK Swedish krona CHF Swiss franc THB Thai baht TL Turkish lira AED U.A.E.

KRW Korean won Mex$ Mexican peso NT$ New Taiwan Dollar NZ$ New Zealand dollar NOK Norwegian krone zł Polish zloty £ Pound sterling lei Romanian new leu RUB Russian Ruble SAR Saudi Arabian riyal.Indian rupee Rp Indonesian rupiah ₪ Israeli new sheqel ¥ Japanese yen JOD Jordanian dinar E£ Egyptian pound € Euro HK$ Hong Kong Dollar HRK Croatian kuna HUF Hungarian forint Rs.AR$ Argentine peso AU$ Australian dollar R$ Brazilian real BGN Bulgarian lev CA$ Canadian dollar CL$ Chilean peso CNY Chinese yuan COL$ Colombian peso Kč Czech koruna DKK Danish krone.dollar £ Pound sterling AU$ Australian dollar The experiment results show the strong superiority of the NAMlet transform for image representation in comparison with some state-of-the-art image sparse representation methods. The NAMlet transform can reduce the lost detail information and remove the restrictions of image size.

In homogeneous blocks, all the pixels are in the same bit-plane. The NAMlets are haar-type wavelets, which are based on the non-symmetric homogeneous blocks obtained by the non-symmetry and anti-packing model. In this paper, we have proposed an image sparse representation method, called NAMlet Transform. Thus, these methods are not only restricted by the size of the image, but also lose a great amount of detail information by using a symmetric blocking method. However, few of the traditional representation methods consider from the point of the anti-packing problem. An efficient sparse representation method can improve the accuracy. Image sparse representation methods have been widely applied in many image processing fields, such as computer vision, image de-noising, super resolution, and visual tracking. Further, an adaptive technique based on binary image characteristics is applied to achieve more compression rates. Moreover, quantisation issue in neural-network deployment is addressed and a solution is proposed. The results of experiments on more than 4000 different images indicate higher compression rate of the proposed structure compared with the commonly used methods such as Comité Consultatif International Téléphonique of Télégraphique (CCITT) G4 and joint bi-level image experts group (JBIG2) standards. In the decompression phase, by applying the pixels locations to the trained network, the output determines the intensity. The final weights of the trained neural-network are quantised, represented by a few bits, Huffman encoded and then stored as the compressed image. The output of the network denotes the pixel intensity (0 or 1). In the proposed lossy compression method, the locations of pixels of image are applied to the inputs of a multilayer perceptron neural-network. This study presents the utilisation of neural-network for bi-level image compression. The compression ratio is increased with the increase of wavelet's passes and with decrease of block size. The results of conducted tests indicated the developed compression system shows outstanding compression performance. Finally, adaptive shift coding is applied to handle the remaining statistical redundancy and attain efficient compression performance. Both scalar quantization and quad tree coding steps are applied on the produced wavelet sub bands. Then, bi-orthogonal wavelet transform is applied to decompose the residue component. Then, the produced cubic Bezier surface is subtracted from the image signal to get the residue component. The proposed method is going to be accomplished using cubic Bezier surface (CBI) representation on wide area of images in order to prune the image component that shows large scale variation. It allows progressive transmission and zooming of the image without need to extra storage. In this paper, an efficient method for compressing color image is presented.