diff --git a/Assignment 2/2020BTEIT00044/bridge.jpg b/Assignment 2/2020BTEIT00044/bridge.jpg
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diff --git a/Assignment 2/2020BTEIT00044/compressd_bridge.jpg b/Assignment 2/2020BTEIT00044/compressd_bridge.jpg
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diff --git a/Assignment 2/2020BTEIT00044/huffman_code.py b/Assignment 2/2020BTEIT00044/huffman_code.py
new file mode 100644
index 0000000..a2a9586
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+++ b/Assignment 2/2020BTEIT00044/huffman_code.py	
@@ -0,0 +1,150 @@
+import re
+import numpy as np
+from PIL import Image
+print("Huffman Compression Program")
+h = int(input("Enter 1 if you want to input an colour image file, 2 for default gray scale case:"))
+if h == 1:
+    file = input("Enter the filename:")
+    my_string = np.asarray(Image.open(file),np.uint8)
+    shape = my_string.shape
+    a = my_string
+    print ("Enetered string is:",my_string)
+    my_string = str(my_string.tolist())
+elif h == 2:
+    array = np.arange(0, 737280, 1, np.uint8)
+    my_string = np.reshape(array, (1024, 720))
+    print ("Enetered string is:",my_string)
+    a = my_string
+    my_string = str(my_string.tolist())
+
+else:
+    print("You entered invalid input")                   
+
+letters = []
+only_letters = []
+for letter in my_string:
+    if letter not in letters:
+        frequency = my_string.count(letter)            
+        letters.append(frequency)
+        letters.append(letter)
+        only_letters.append(letter)
+
+nodes = []
+while len(letters) > 0:
+    nodes.append(letters[0:2])
+    letters = letters[2:]                               
+nodes.sort()
+huffman_tree = []
+huffman_tree.append(nodes)                             
+
+def combine_nodes(nodes):
+    pos = 0
+    newnode = []
+    if len(nodes) > 1:
+        nodes.sort()
+        nodes[pos].append("1")                       
+        nodes[pos+1].append("0")
+        combined_node1 = (nodes[pos] [0] + nodes[pos+1] [0])
+        combined_node2 = (nodes[pos] [1] + nodes[pos+1] [1])  
+        newnode.append(combined_node1)
+        newnode.append(combined_node2)
+        newnodes=[]
+        newnodes.append(newnode)
+        newnodes = newnodes + nodes[2:]
+        nodes = newnodes
+        huffman_tree.append(nodes)
+        combine_nodes(nodes)
+    return huffman_tree                                     
+
+newnodes = combine_nodes(nodes)
+
+huffman_tree.sort(reverse = True)
+print("Huffman tree with merged pathways:")
+
+checklist = []
+for level in huffman_tree:
+    for node in level:
+        if node not in checklist:
+            checklist.append(node)
+        else:
+            level.remove(node)
+count = 0
+for level in huffman_tree:
+    print("Level", count,":",level)             
+    count+=1
+print()
+
+letter_binary = []
+if len(only_letters) == 1:
+    lettercode = [only_letters[0], "0"]
+    letter_binary.append(letter_code*len(my_string))
+else:
+    for letter in only_letters:
+        code =""
+        for node in checklist:
+            if len (node)>2 and letter in node[1]:           
+                code = code + node[2]
+        lettercode =[letter,code]
+        letter_binary.append(lettercode)
+print(letter_binary)
+print("Binary code generated:")
+for letter in letter_binary:
+    print(letter[0], letter[1])
+
+bitstring =""
+for character in my_string:
+    for item in letter_binary:
+        if character in item:
+            bitstring = bitstring + item[1]
+binary ="0b"+bitstring
+print("Your message as binary is:")
+                                        
+
+uncompressed_file_size = len(my_string)*7
+compressed_file_size = len(binary)-2
+print("Your original file size was", uncompressed_file_size,"bits. The compressed size is:",compressed_file_size)
+print("This is a saving of ",uncompressed_file_size-compressed_file_size,"bits")
+output = open("compressed.txt","w+")
+print("Compressed file generated as compressed.txt")
+output = open("compressed.txt","w+")
+print("Decoding.......")
+output.write(bitstring)
+
+bitstring = str(binary[2:])
+uncompressed_string =""
+code =""
+for digit in bitstring:
+    code = code+digit
+    pos=0                                        
+    for letter in letter_binary:
+        if code ==letter[1]:
+            uncompressed_string=uncompressed_string+letter_binary[pos] [0]
+            code=""
+        pos+=1
+
+print("Your UNCOMPRESSED data is:")
+if h == 1:
+    temp = re.findall(r'\d+', uncompressed_string)
+    res = list(map(int, temp))
+    res = np.array(res)
+    res = res.astype(np.uint8)
+    res = np.reshape(res, shape)
+    print(res)
+    print("Observe the shapes and input and output arrays are matching or not")
+    print("Input image dimensions:",shape)
+    print("Output image dimensions:",res.shape)
+    data = Image.fromarray(res)
+    data.save('uncompressed.png')
+    if a.all() == res.all():
+        print("Success")
+if h == 2:
+    temp = re.findall(r'\d+', uncompressed_string)
+    res = list(map(int, temp))
+    print(res)
+    res = np.array(res)
+    res = res.astype(np.uint8)
+    res = np.reshape(res, (1024, 720))
+    print(res)
+    data = Image.fromarray(res)
+    data.save('uncompressed.png')
+    print("Success")
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diff --git a/Assignment 2/2020BTEIT00044/observation.txt b/Assignment 2/2020BTEIT00044/observation.txt
new file mode 100644
index 0000000..313c48c
--- /dev/null
+++ b/Assignment 2/2020BTEIT00044/observation.txt	
@@ -0,0 +1,2 @@
+Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression.
+
diff --git a/Assignment 2/2020BTEIT00044/vector_quantization.py b/Assignment 2/2020BTEIT00044/vector_quantization.py
new file mode 100644
index 0000000..a55b272
--- /dev/null
+++ b/Assignment 2/2020BTEIT00044/vector_quantization.py	
@@ -0,0 +1,42 @@
+
+import numpy as np
+import scipy as sp
+
+import matplotlib.pyplot as plt
+
+from sklearn import cluster
+
+from PIL import Image
+
+im = Image.open("myImage.jpg")
+im = np.array(im)
+
+
+n_clusters = 5
+np.random.seed(0)
+
+X = im.reshape((-1, 1))  
+k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
+k_means.fit(X)
+values = k_means.cluster_centers_.squeeze()
+labels = k_means.labels_
+
+
+im_compressed = np.choose(labels, values)
+im_compressed.shape = im.shape
+
+vmin = im.min()
+vmax = im.max()
+
+
+plt.figure(1, figsize=(3, 2.2))
+plt.imshow(im.astype('uint8'), cmap=plt.cm.gray, vmin=vmin, vmax=256, )
+
+
+plt.figure(2, figsize=(3, 2.2))
+plt.imshow(im_compressed.astype('uint8'),
+           cmap=plt.cm.gray, vmin=vmin, vmax=vmax, )
+Image.fromarray((im_compressed).astype("uint8")).save("compressed.png")
+
+
+plt.show()
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