1. A wavelet coefficient quantization method using a human visual model in image compression, comprising: Step 1: performing wavelet transform on an image to be compressed, and partitioning all obtained sub-bands into n×n blocks, and marking each block by B(j,d,i), wherein j represents a wavelet transform level of the sub-band where the block is located, d represents a direction of the sub-band where the block is located, and i represents a position of the block in the sub-band; Step 2: calculating a maximal value MAX k and a minimal value MIN k of wavelet coefficients in the lowest frequency sub-band LL k after the k th level wavelet transform, and a mean M(i) of coefficients of each block in the sub-band, then mapping the obtained mean M(i) of coefficients of each block to obtain a mapped gray value L(i): 
 L ( i )=255×( M ( i )−MIN k )/(MAX k −MIN k ) calculating a luminance component T A (j,d,i) of the visual model according to the obtained mapped gray value L(i): T A ⁡ ( j , d , i ) = { w ⁡ ( a + b · T 1 ) c ⁢ / ⁢ g , L ⁡ ( i ) ≤ T 1 w ⁡ ( a + b · L ⁡ ( i ) ) c ⁢ / ⁢ g , T 1 < L ⁡ ( i ) < T 2 w ⁡ ( a + b · T 2 ) c ⁢ / ⁢ g , L ⁡ ( i ) ≥ T 2 wherein a is a voltage when a pixel value of a Cathode Ray Tube (CRT) display is 0, b is a scale factor between a pixel value and a voltage value, c is a gamma value of the display, w is the Weber ratio, g is an energy gain factor at the k th level of wavelet transform, and T 1 and T 2 are respectively upper and lower thresholds of a linear region in Weber's Law; Step 3: respectively obtaining blocks of HL and LH sub-bands of the image to be compressed after the second and third levels of wavelet transform, and calculating a masking component T M (j,d,i) of the visual model; T M ⁡ ( j , d , i ) = C ⁡ ( 2 , HL , i ) + C ⁡ ( 3 , HL , i ) + C ⁡ ( 2 , LH , i ) + C ⁡ ( 3 , LH , i ) 2 ⁢ 2 wherein C(2,HL,i) represents a maximal value of absolute values of coefficients in a block B(2,HL,i), C(3,HL,i) represents a maximal value of absolute values of coefficients in a block B(3,HL,i), C(2,LH,i) represents a maximal value of absolute values of coefficients in a block B(2,LH,i), and C(3,LH,i) represents a maximal value of absolute values of coefficients in a block B(3,LH,i); Step 4: calculating a joint component T G (j,d,i) of the obtained luminance component and masking component: 
 T G ( j,d,i )=[ T A ( j,d,i )] α [T M ( j,d,i )] β wherein α and β respectively represent luminance component and masking component weight values; Step 5: quantizing the obtained joint component T G (j,d,i) according to predetermined N levels, to obtain N level values T G1 to T GN , and a quantized value Q(j,d,i) of each joint component T G (j,d,i), and respectively coding the level values T G1 to T GN and the value Q(j,d,i); Step 6: selecting multiple test images of the same size as the image to be compressed, using the wavelet transform method to obtain wavelet coefficients, and calculating a frequency component of the visual model for each an respectively in the following manner: adjusting a quantized value to quantize the band until distortion of a recovered image is just perceptible to human eyes, recording the current quantized value as a frequency Just Noticeable Differences (JND) value of the image at the band, and averaging the frequency JND values of the same bands in the multiple images to obtain a frequency component T S (j,d,i) of the visual model; Step 7: using the obtained quantized value Q(j,d,i) of the joint component T G (j,d,i) and the frequency component T S (j,d,i) of the visual model to calculate a visual quantization threshold T(j,d,i): 
 T ( j,d,i )= Q ( j,d,i )·[ T S ( j,d,i )] γ wherein γ represents a frequency component weight value; and Step 8: performing quantization using the visual quantization threshold.